Tag Archives: essays

COVID-19 and Acedia

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/10/covid-19-and-acedia.html

Note: This isn’t my usual essay topic. Still, I want to put it on my blog.

Six months into the pandemic with no end in sight, many of us have been feeling a sense of unease that goes beyond anxiety or distress. It’s a nameless feeling that somehow makes it hard to go on with even the nice things we regularly do.

What’s blocking our everyday routines is not the anxiety of lockdown adjustments, or the worries about ourselves and our loved ones — real though those worries are. It isn’t even the sense that, if we’re really honest with ourselves, much of what we do is pretty self-indulgent when held up against the urgency of a global pandemic.

It is something more troubling and harder to name: an uncertainty about why we would go on doing much of what for years we’d taken for granted as inherently valuable.

What we are confronting is something many writers in the pandemic have approached from varying angles: a restless distraction that stems not just from not knowing when it will all end, but also from not knowing what that end will look like. Perhaps the sharpest insight into this feeling has come from Jonathan Zecher, a historian of religion, who linked it to the forgotten Christian term: acedia.

Acedia was a malady that apparently plagued many medieval monks. It’s a sense of no longer caring about caring, not because one had become apathetic, but because somehow the whole structure of care had become jammed up.

What could this particular form of melancholy mean in an urgent global crisis? On the face of it, all of us care very much about the health risks to those we know and don’t know. Yet lurking alongside such immediate cares is a sense of dislocation that somehow interferes with how we care.

The answer can be found in an extreme thought experiment about death. In 2013, philosopher Samuel Scheffler explored a core assumption about death. We all assume that there will be a future world that survives our particular life, a world populated by people roughly like us, including some who are related to us or known to us. Though we rarely or acknowledge it, this presumed future world is the horizon towards which everything we do in the present is oriented.

But what, Scheffler asked, if we lose that assumed future world — because, say, we are told that human life will end on a fixed date not far after our own death? Then the things we value would start to lose their value. Our sense of why things matter today is built on the presumption that they will continue to matter in the future, even when we ourselves are no longer around to value them.

Our present relations to people and things are, in this deep way, future-oriented. Symphonies are written, buildings built, children conceived in the present, but always with a future in mind. What happens to our ethical bearings when we start to lose our grip on that future?

It’s here, moving back to the particular features of the global pandemic, that we see more clearly what drives the restlessness and dislocation so many have been feeling. The source of our current acedia is not the literal loss of a future; even the most pessimistic scenarios surrounding COVID-19 have our species surviving. The dislocation is more subtle: a disruption in pretty much every future frame of reference on which just going on in the present relies.

Moving around is what we do as creatures, and for that we need horizons. COVID-19 has erased many of the spatial and temporal horizons we rely on, even if we don’t notice them very often. We don’t know how the economy will look, how social life will go on, how our home routines will be changed, how work will be organized, how universities or the arts or local commerce will survive.

What unsettles us is not only fear of change. It’s that, if we can no longer trust in the future, many things become irrelevant, retrospectively pointless. And by that we mean from the perspective of a future whose basic shape we can no longer take for granted. This fundamentally disrupts how we weigh the value of what we are doing right now. It becomes especially hard under these conditions to hold on to the value in activities that, by their very nature, are future-directed, such as education or institution-building.

That’s what many of us are feeling. That’s today’s acedia.

Naming this malaise may seem more trouble than its worth, but the opposite is true. Perhaps the worst thing about medieval acedia was that monks struggled with its dislocation in isolation. But today’s disruption of our sense of a future must be a shared challenge. Because what’s disrupted is the structure of care that sustains why we go on doing things together, and this can only be repaired through renewed solidarity.

Such solidarity, however, has one precondition: that we openly discuss the problem of acedia, and how it prevents us from facing our deepest future uncertainties. Once we have done that, we can recognize it as a problem we choose to face together — across political and cultural lines — as families, communities, nations and a global humanity. Which means doing so in acceptance of our shared vulnerability, rather than suffering each on our own.

This essay was written with Nick Couldry, and previously appeared on CNN.com.

On the Twitter Hack

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/07/on_the_twitter_.html

Twitter was hacked this week. Not a few people’s Twitter accounts, but all of Twitter. Someone compromised the entire Twitter network, probably by stealing the log-in credentials of one of Twitter’s system administrators. Those are the people trusted to ensure that Twitter functions smoothly.

The hacker used that access to send tweets from a variety of popular and trusted accounts, including those of Joe Biden, Bill Gates, and Elon Musk, as part of a mundane scam — stealing bitcoin — but it’s easy to envision more nefarious scenarios. Imagine a government using this sort of attack against another government, coordinating a series of fake tweets from hundreds of politicians and other public figures the day before a major election, to affect the outcome. Or to escalate an international dispute. Done well, it would be devastating.

Whether the hackers had access to Twitter direct messages is not known. These DMs are not end-to-end encrypted, meaning that they are unencrypted inside Twitter’s network and could have been available to the hackers. Those messages — between world leaders, industry CEOs, reporters and their sources, heath organizations — are much more valuable than bitcoin. (If I were a national-intelligence agency, I might even use a bitcoin scam to mask my real intelligence-gathering purpose.) Back in 2018, Twitter said it was exploring encrypting those messages, but it hasn’t yet.

Internet communications platforms — such as Facebook, Twitter, and YouTube — are crucial in today’s society. They’re how we communicate with one another. They’re how our elected leaders communicate with us. They are essential infrastructure. Yet they are run by for-profit companies with little government oversight. This is simply no longer sustainable. Twitter and companies like it are essential to our national dialogue, to our economy, and to our democracy. We need to start treating them that way, and that means both requiring them to do a better job on security and breaking them up.

In the Twitter case this week, the hacker’s tactics weren’t particularly sophisticated. We will almost certainly learn about security lapses at Twitter that enabled the hack, possibly including a SIM-swapping attack that targeted an employee’s cellular service provider, or maybe even a bribed insider. The FBI is investigating.

This kind of attack is known as a “class break.” Class breaks are endemic to computerized systems, and they’re not something that we as users can defend against with better personal security. It didn’t matter whether individual accounts had a complicated and hard-to-remember password, or two-factor authentication. It didn’t matter whether the accounts were normally accessed via a Mac or a PC. There was literally nothing any user could do to protect against it.

Class breaks are security vulnerabilities that break not just one system, but an entire class of systems. They might exploit a vulnerability in a particular operating system that allows an attacker to take remote control of every computer that runs on that system’s software. Or a vulnerability in internet-enabled digital video recorders and webcams that allows an attacker to recruit those devices into a massive botnet. Or a single vulnerability in the Twitter network that allows an attacker to take over every account.

For Twitter users, this attack was a double whammy. Many people rely on Twitter’s authentication systems to know that someone who purports to be a certain celebrity, politician, or journalist is really that person. When those accounts were hijacked, trust in that system took a beating. And then, after the attack was discovered and Twitter temporarily shut down all verified accounts, the public lost a vital source of information.

There are many security technologies companies like Twitter can implement to better protect themselves and their users; that’s not the issue. The problem is economic, and fixing it requires doing two things. One is regulating these companies, and requiring them to spend more money on security. The second is reducing their monopoly power.

The security regulations for banks are complex and detailed. If a low-level banking employee were caught messing around with people’s accounts, or if she mistakenly gave her log-in credentials to someone else, the bank would be severely fined. Depending on the details of the incident, senior banking executives could be held personally liable. The threat of these actions helps keep our money safe. Yes, it costs banks money; sometimes it severely cuts into their profits. But the banks have no choice.

The opposite is true for these tech giants. They get to decide what level of security you have on your accounts, and you have no say in the matter. If you are offered security and privacy options, it’s because they decided you can have them. There is no regulation. There is no accountability. There isn’t even any transparency. Do you know how secure your data is on Facebook, or in Apple’s iCloud, or anywhere? You don’t. No one except those companies do. Yet they’re crucial to the country’s national security. And they’re the rare consumer product or service allowed to operate without significant government oversight.

For example, President Donald Trump’s Twitter account wasn’t hacked as Joe Biden’s was, because that account has “special protections,” the details of which we don’t know. We also don’t know what other world leaders have those protections, or the decision process surrounding who gets them. Are they manual? Can they scale? Can all verified accounts have them? Your guess is as good as mine.

In addition to security measures, the other solution is to break up the tech monopolies. Companies like Facebook and Twitter have so much power because they are so large, and they face no real competition. This is a national-security risk as well as a personal-security risk. Were there 100 different Twitter-like companies, and enough compatibility so that all their feeds could merge into one interface, this attack wouldn’t have been such a big deal. More important, the risk of a similar but more politically targeted attack wouldn’t be so great. If there were competition, different platforms would offer different security options, as well as different posting rules, different authentication guidelines — different everything. Competition is how our economy works; it’s how we spur innovation. Monopolies have more power to do what they want in the quest for profits, even if it harms people along the way.

This wasn’t Twitter’s first security problem involving trusted insiders. In 2017, on his last day of work, an employee shut down President Donald Trump’s account. In 2019, two people were charged with spying for the Saudi government while they were Twitter employees.

Maybe this hack will serve as a wake-up call. But if past incidents involving Twitter and other companies are any indication, it won’t. Underspending on security, and letting society pay the eventual price, is far more profitable. I don’t blame the tech companies. Their corporate mandate is to make as much money as is legally possible. Fixing this requires changes in the law, not changes in the hearts of the company’s leaders.

This essay previously appeared on TheAtlantic.com.

The Security Value of Inefficiency

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/07/the_security_va.html

For decades, we have prized efficiency in our economy. We strive for it. We reward it. In normal times, that’s a good thing. Running just at the margins is efficient. A single just-in-time global supply chain is efficient. Consolidation is efficient. And that’s all profitable. Inefficiency, on the other hand, is waste. Extra inventory is inefficient. Overcapacity is inefficient. Using many small suppliers is inefficient. Inefficiency is unprofitable.

But inefficiency is essential security, as the COVID-19 pandemic is teaching us. All of the overcapacity that has been squeezed out of our healthcare system; we now wish we had it. All of the redundancy in our food production that has been consolidated away; we want that, too. We need our old, local supply chains — not the single global ones that are so fragile in this crisis. And we want our local restaurants and businesses to survive, not just the national chains.

We have lost much inefficiency to the market in the past few decades. Investors have become very good at noticing any fat in every system and swooping down to monetize those redundant assets. The winner-take-all mentality that has permeated so many industries squeezes any inefficiencies out of the system.

This drive for efficiency leads to brittle systems that function properly when everything is normal but break under stress. And when they break, everyone suffers. The less fortunate suffer and die. The more fortunate are merely hurt, and perhaps lose their freedoms or their future. But even the extremely fortunate suffer — maybe not in the short term, but in the long term from the constriction of the rest of society.

Efficient systems have limited ability to deal with system-wide economic shocks. Those shocks are coming with increased frequency. They’re caused by global pandemics, yes, but also by climate change, by financial crises, by political crises. If we want to be secure against these crises and more, we need to add inefficiency back into our systems.

I don’t simply mean that we need to make our food production, or healthcare system, or supply chains sloppy and wasteful. We need a certain kind of inefficiency, and it depends on the system in question. Sometimes we need redundancy. Sometimes we need diversity. Sometimes we need overcapacity.

The market isn’t going to supply any of these things, least of all in a strategic capacity that will result in resilience. What’s necessary to make any of this work is regulation.

First, we need to enforce antitrust laws. Our meat supply chain is brittle because there are limited numbers of massive meatpacking plants — now disease factories — rather than lots of smaller slaughterhouses. Our retail supply chain is brittle because a few national companies and websites dominate. We need multiple companies offering alternatives to a single product or service. We need more competition, more niche players. We need more local companies, more domestic corporate players, and diversity in our international suppliers. Competition provides all of that, while monopolies suck that out of the system.

The second thing we need is specific regulations that require certain inefficiencies. This isn’t anything new. Every safety system we have is, to some extent, an inefficiency. This is true for fire escapes on buildings, lifeboats on cruise ships, and multiple ways to deploy the landing gear on aircraft. Not having any of those things would make the underlying systems more efficient, but also less safe. It’s also true for the internet itself, originally designed with extensive redundancy as a Cold War security measure.

With those two things in place, the market can work its magic to provide for these strategic inefficiencies as cheaply and as effectively as possible. As long as there are competitors who are vying with each other, and there aren’t competitors who can reduce the inefficiencies and undercut the competition, these inefficiencies just become part of the price of whatever we’re buying.

The government is the entity that steps in and enforces a level playing field instead of a race to the bottom. Smart regulation addresses the long-term need for security, and ensures it’s not continuously sacrificed to short-term considerations.

We have largely been content to ignore the long term and let Wall Street run our economy as efficiently as it can. That’s no longer sustainable. We need inefficiency — the right kind in the right way — to ensure our security. No, it’s not free. But it’s worth the cost.

This essay previously appeared in Quartz.

Security of Health Information

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/03/security_of_hea.html

The world is racing to contain the new COVID-19 virus that is spreading around the globe with alarming speed. Right now, pandemic disease experts at the World Health Organization (WHO), the US Centers for Disease Control and Prevention (CDC), and other public-health agencies are gathering information to learn how and where the virus is spreading. To do so, they are using a variety of digital communications and surveillance systems. Like much of the medical infrastructure, these systems are highly vulnerable to hacking and interference.

That vulnerability should be deeply concerning. Governments and intelligence agencies have long had an interest in manipulating health information, both in their own countries and abroad. They might do so to prevent mass panic, avert damage to their economies, or avoid public discontent (if officials made grave mistakes in containing an outbreak, for example). Outside their borders, states might use disinformation to undermine their adversaries or disrupt an alliance between other nations. A sudden epidemic­ — when countries struggle to manage not just the outbreak but its social, economic, and political fallout­ — is especially tempting for interference.

In the case of COVID-19, such interference is already well underway. That fact should not come as a surprise. States hostile to the West have a long track record of manipulating information about health issues to sow distrust. In the 1980s, for example, the Soviet Union spread the false story that the US Department of Defense bioengineered HIV in order to kill African Americans. This propaganda was effective: some 20 years after the original Soviet disinformation campaign, a 2005 survey found that 48 percent of African Americans believed HIV was concocted in a laboratory, and 15 percent thought it was a tool of genocide aimed at their communities.

More recently, in 2018, Russia undertook an extensive disinformation campaign to amplify the anti-vaccination movement using social media platforms like Twitter and Facebook. Researchers have confirmed that Russian trolls and bots tweeted anti-vaccination messages at up to 22 times the rate of average users. Exposure to these messages, other researchers found, significantly decreased vaccine uptake, endangering individual lives and public health.

Last week, US officials accused Russia of spreading disinformation about COVID-19 in yet another coordinated campaign. Beginning around the middle of January, thousands of Twitter, Facebook, and Instagram accounts­ — many of which had previously been tied to Russia­ — had been seen posting nearly identical messages in English, German, French, and other languages, blaming the United States for the outbreak. Some of the messages claimed that the virus is part of a US effort to wage economic war on China, others that it is a biological weapon engineered by the CIA.

As much as this disinformation can sow discord and undermine public trust, the far greater vulnerability lies in the United States’ poorly protected emergency-response infrastructure, including the health surveillance systems used to monitor and track the epidemic. By hacking these systems and corrupting medical data, states with formidable cybercapabilities can change and manipulate data right at the source.

Here is how it would work, and why we should be so concerned. Numerous health surveillance systems are monitoring the spread of COVID-19 cases, including the CDC’s influenza surveillance network. Almost all testing is done at a local or regional level, with public-health agencies like the CDC only compiling and analyzing the data. Only rarely is an actual biological sample sent to a high-level government lab. Many of the clinics and labs providing results to the CDC no longer file reports as in the past, but have several layers of software to store and transmit the data.

Potential vulnerabilities in these systems are legion: hackers exploiting bugs in the software, unauthorized access to a lab’s servers by some other route, or interference with the digital communications between the labs and the CDC. That the software involved in disease tracking sometimes has access to electronic medical records is particularly concerning, because those records are often integrated into a clinic or hospital’s network of digital devices. One such device connected to a single hospital’s network could, in theory, be used to hack into the CDC’s entire COVID-19 database.

In practice, hacking deep into a hospital’s systems can be shockingly easy. As part of a cybersecurity study, Israeli researchers at Ben-Gurion University were able to hack into a hospital’s network via the public Wi-Fi system. Once inside, they could move through most of the hospital’s databases and diagnostic systems. Gaining control of the hospital’s unencrypted image database, the researchers inserted malware that altered healthy patients’ CT scans to show nonexistent tumors. Radiologists reading these images could only distinguish real from altered CTs 60 percent of the time­ — and only after being alerted that some of the CTs had been manipulated.

Another study directly relevant to public-health emergencies showed that a critical US biosecurity initiative, the Department of Homeland Security’s BioWatch program, had been left vulnerable to cyberattackers for over a decade. This program monitors more than 30 US jurisdictions and allows health officials to rapidly detect a bioweapons attack. Hacking this program could cover up an attack, or fool authorities into believing one has occurred.

Fortunately, no case of healthcare sabotage by intelligence agencies or hackers has come to light (the closest has been a series of ransomware attacks extorting money from hospitals, causing significant data breaches and interruptions in medical services). But other critical infrastructure has often been a target. The Russians have repeatedly hacked Ukraine’s national power grid, and have been probing US power plants and grid infrastructure as well. The United States and Israel hacked the Iranian nuclear program, while Iran has targeted Saudi Arabia’s oil infrastructure. There is no reason to believe that public-health infrastructure is in any way off limits.

Despite these precedents and proven risks, a detailed assessment of the vulnerability of US health surveillance systems to infiltration and manipulation has yet to be made. With COVID-19 on the verge of becoming a pandemic, the United States is at risk of not having trustworthy data, which in turn could cripple our country’s ability to respond.

Under normal conditions, there is plenty of time for health officials to notice unusual patterns in the data and track down wrong information­ — if necessary, using the old-fashioned method of giving the lab a call. But during an epidemic, when there are tens of thousands of cases to track and analyze, it would be easy for exhausted disease experts and public-health officials to be misled by corrupted data. The resulting confusion could lead to misdirected resources, give false reassurance that case numbers are falling, or waste precious time as decision makers try to validate inconsistent data.

In the face of a possible global pandemic, US and international public-health leaders must lose no time assessing and strengthening the security of the country’s digital health systems. They also have an important role to play in the broader debate over cybersecurity. Making America’s health infrastructure safe requires a fundamental reorientation of cybersecurity away from offense and toward defense. The position of many governments, including the United States’, that Internet infrastructure must be kept vulnerable so they can better spy on others, is no longer tenable. A digital arms race, in which more countries acquire ever more sophisticated cyberattack capabilities, only increases US vulnerability in critical areas such as pandemic control. By highlighting the importance of protecting digital health infrastructure, public-health leaders can and should call for a well-defended and peaceful Internet as a foundation for a healthy and secure world.

This essay was co-authored with Margaret Bourdeaux; a slightly different version appeared in Foreign Policy.

EDITED TO ADD: On last week’s squid post, there was a big conversation regarding the COVID-19. Many of the comments straddled the line between what are and aren’t the the core topics. Yesterday I deleted a bunch for being off-topic. Then I reconsidered and republished some of what I deleted.

Going forward, comments about the COVID-19 will be restricted to the security and risk implications of the virus. This includes cybersecurity, security, risk management, surveillance, and containment measures. Comments that stray off those topics will be removed. By clarifying this, I hope to keep the conversation on-topic while also allowing discussion of the security implications of current events.

Thank you for your patience and forbearance on this.

Modern Mass Surveillance: Identify, Correlate, Discriminate

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/01/modern_mass_sur.html

Communities across the United States are starting to ban facial recognition technologies. In May of last year, San Francisco banned facial recognition; the neighboring city of Oakland soon followed, as did Somerville and Brookline in Massachusetts (a statewide ban may follow). In December, San Diego suspended a facial recognition program in advance of a new statewide law, which declared it illegal, coming into effect. Forty major music festivals pledged not to use the technology, and activists are calling for a nationwide ban. Many Democratic presidential candidates support at least a partial ban on the technology.

These efforts are well-intentioned, but facial recognition bans are the wrong way to fight against modern surveillance. Focusing on one particular identification method misconstrues the nature of the surveillance society we’re in the process of building. Ubiquitous mass surveillance is increasingly the norm. In countries like China, a surveillance infrastructure is being built by the government for social control. In countries like the United States, it’s being built by corporations in order to influence our buying behavior, and is incidentally used by the government.

In all cases, modern mass surveillance has three broad components: identification, correlation and discrimination. Let’s take them in turn.

Facial recognition is a technology that can be used to identify people without their knowledge or consent. It relies on the prevalence of cameras, which are becoming both more powerful and smaller, and machine learning technologies that can match the output of these cameras with images from a database of existing photos.

But that’s just one identification technology among many. People can be identified at a distance by their heartbeat or by their gait, using a laser-based system. Cameras are so good that they can read fingerprints and iris patterns from meters away. And even without any of these technologies, we can always be identified because our smartphones broadcast unique numbers called MAC addresses. Other things identify us as well: our phone numbers, our credit card numbers, the license plates on our cars. China, for example, uses multiple identification technologies to support its surveillance state.

Once we are identified, the data about who we are and what we are doing can be correlated with other data collected at other times. This might be movement data, which can be used to “follow” us as we move throughout our day. It can be purchasing data, Internet browsing data, or data about who we talk to via email or text. It might be data about our income, ethnicity, lifestyle, profession and interests. There is an entire industry of data brokers who make a living analyzing and augmenting data about who we are ­– using surveillance data collected by all sorts of companies and then sold without our knowledge or consent.

There is a huge ­– and almost entirely unregulated ­– data broker industry in the United States that trades on our information. This is how large Internet companies like Google and Facebook make their money. It’s not just that they know who we are, it’s that they correlate what they know about us to create profiles about who we are and what our interests are. This is why many companies buy license plate data from states. It’s also why companies like Google are buying health records, and part of the reason Google bought the company Fitbit, along with all of its data.

The whole purpose of this process is for companies –­ and governments ­– to treat individuals differently. We are shown different ads on the Internet and receive different offers for credit cards. Smart billboards display different advertisements based on who we are. In the future, we might be treated differently when we walk into a store, just as we currently are when we visit websites.

The point is that it doesn’t matter which technology is used to identify people. That there currently is no comprehensive database of heartbeats or gaits doesn’t make the technologies that gather them any less effective. And most of the time, it doesn’t matter if identification isn’t tied to a real name. What’s important is that we can be consistently identified over time. We might be completely anonymous in a system that uses unique cookies to track us as we browse the Internet, but the same process of correlation and discrimination still occurs. It’s the same with faces; we can be tracked as we move around a store or shopping mall, even if that tracking isn’t tied to a specific name. And that anonymity is fragile: If we ever order something online with a credit card, or purchase something with a credit card in a store, then suddenly our real names are attached to what was anonymous tracking information.

Regulating this system means addressing all three steps of the process. A ban on facial recognition won’t make any difference if, in response, surveillance systems switch to identifying people by smartphone MAC addresses. The problem is that we are being identified without our knowledge or consent, and society needs rules about when that is permissible.

Similarly, we need rules about how our data can be combined with other data, and then bought and sold without our knowledge or consent. The data broker industry is almost entirely unregulated; there’s only one law ­– passed in Vermont in 2018 ­– that requires data brokers to register and explain in broad terms what kind of data they collect. The large Internet surveillance companies like Facebook and Google collect dossiers on us are more detailed than those of any police state of the previous century. Reasonable laws would prevent the worst of their abuses.

Finally, we need better rules about when and how it is permissible for companies to discriminate. Discrimination based on protected characteristics like race and gender is already illegal, but those rules are ineffectual against the current technologies of surveillance and control. When people can be identified and their data correlated at a speed and scale previously unseen, we need new rules.

Today, facial recognition technologies are receiving the brunt of the tech backlash, but focusing on them misses the point. We need to have a serious conversation about all the technologies of identification, correlation and discrimination, and decide how much we as a society want to be spied on by governments and corporations — and what sorts of influence we want them to have over our lives.

This essay previously appeared in the New York Times.

EDITED TO ADD: Rereading this post-publication, I see that it comes off as overly critical of those who are doing activism in this space. Writing the piece, I wasn’t thinking about political tactics. I was thinking about the technologies that support surveillance capitalism, and law enforcement’s usage of that corporate platform. Of course it makes sense to focus on face recognition in the short term. It’s something that’s easy to explain, viscerally creepy, and obviously actionable. It also makes sense to focus specifically on law enforcement’s use of the technology; there are clear civil and constitutional rights issues. The fact that law enforcement is so deeply involved in the technology’s marketing feels wrong. And the technology is currently being deployed in Hong Kong against political protesters. It’s why the issue has momentum, and why we’ve gotten the small wins we’ve had. (The EU is considering a five-year ban on face recognition technologies.) Those wins build momentum, which lead to more wins. I should have been kinder to those in the trenches.

If you want to help, sign the petition from Public Voice calling on a moratorium on facial recognition technology for mass surveillance. Or write to your US congressperson and demand similar action. There’s more information from EFF and EPIC.

Artificial Personas and Public Discourse

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/01/artificial_pers.html

Presidential campaign season is officially, officially, upon us now, which means it’s time to confront the weird and insidious ways in which technology is warping politics. One of the biggest threats on the horizon: artificial personas are coming, and they’re poised to take over political debate. The risk arises from two separate threads coming together: artificial intelligence-driven text generation and social media chatbots. These computer-generated “people” will drown out actual human discussions on the Internet.

Text-generation software is already good enough to fool most people most of the time. It’s writing news stories, particularly in sports and finance. It’s talking with customers on merchant websites. It’s writing convincing op-eds on topics in the news (though there are limitations). And it’s being used to bulk up “pink-slime journalism” — websites meant to appear like legitimate local news outlets but that publish propaganda instead.

There’s a record of algorithmic content pretending to be from individuals, as well. In 2017, the Federal Communications Commission had an online public-commenting period for its plans to repeal net neutrality. A staggering 22 million comments were received. Many of them — maybe half — were fake, using stolen identities. These comments were also crude; 1.3 million were generated from the same template, with some words altered to make them appear unique. They didn’t stand up to even cursory scrutiny.

These efforts will only get more sophisticated. In a recent experiment, Harvard senior Max Weiss used a text-generation program to create 1,000 comments in response to a government call on a Medicaid issue. These comments were all unique, and sounded like real people advocating for a specific policy position. They fooled the Medicaid.gov administrators, who accepted them as genuine concerns from actual human beings. This being research, Weiss subsequently identified the comments and asked for them to be removed, so that no actual policy debate would be unfairly biased. The next group to try this won’t be so honorable.

Chatbots have been skewing social-media discussions for years. About a fifth of all tweets about the 2016 presidential election were published by bots, according to one estimate, as were about a third of all tweets about that year’s Brexit vote. An Oxford Internet Institute report from last year found evidence of bots being used to spread propaganda in 50 countries. These tended to be simple programs mindlessly repeating slogans: a quarter million pro-Saudi “We all have trust in Mohammed bin Salman” tweets following the 2018 murder of Jamal Khashoggi, for example. Detecting many bots with a few followers each is harder than detecting a few bots with lots of followers. And measuring the effectiveness of these bots is difficult. The best analyses indicate that they did not affect the 2016 US presidential election. More likely, they distort people’s sense of public sentiment and their faith in reasoned political debate. We are all in the middle of a novel social experiment.

Over the years, algorithmic bots have evolved to have personas. They have fake names, fake bios, and fake photos — sometimes generated by AI. Instead of endlessly spewing propaganda, they post only occasionally. Researchers can detect that these are bots and not people, based on their patterns of posting, but the bot technology is getting better all the time, outpacing tracking attempts. Future groups won’t be so easily identified. They’ll embed themselves in human social groups better. Their propaganda will be subtle, and interwoven in tweets about topics relevant to those social groups.

Combine these two trends and you have the recipe for nonhuman chatter to overwhelm actual political speech.

Soon, AI-driven personas will be able to write personalized letters to newspapers and elected officials, submit individual comments to public rule-making processes, and intelligently debate political issues on social media. They will be able to comment on social-media posts, news sites, and elsewhere, creating persistent personas that seem real even to someone scrutinizing them. They will be able to pose as individuals on social media and send personalized texts. They will be replicated in the millions and engage on the issues around the clock, sending billions of messages, long and short. Putting all this together, they’ll be able to drown out any actual debate on the Internet. Not just on social media, but everywhere there’s commentary.

Maybe these persona bots will be controlled by foreign actors. Maybe it’ll be domestic political groups. Maybe it’ll be the candidates themselves. Most likely, it’ll be everybody. The most important lesson from the 2016 election about misinformation isn’t that misinformation occurred; it is how cheap and easy misinforming people was. Future technological improvements will make it all even more affordable.

Our future will consist of boisterous political debate, mostly bots arguing with other bots. This is not what we think of when we laud the marketplace of ideas, or any democratic political process. Democracy requires two things to function properly: information and agency. Artificial personas can starve people of both.

Solutions are hard to imagine. We can regulate the use of bots — a proposed California law would require bots to identify themselves — but that is effective only against legitimate influence campaigns, such as advertising. Surreptitious influence operations will be much harder to detect. The most obvious defense is to develop and standardize better authentication methods. If social networks verify that an actual person is behind each account, then they can better weed out fake personas. But fake accounts are already regularly created for real people without their knowledge or consent, and anonymous speech is essential for robust political debate, especially when speakers are from disadvantaged or marginalized communities. We don’t have an authentication system that both protects privacy and scales to the billions of users.

We can hope that our ability to identify artificial personas keeps up with our ability to disguise them. If the arms race between deep fakes and deep-fake detectors is any guide, that’ll be hard as well. The technologies of obfuscation always seem one step ahead of the technologies of detection. And artificial personas will be designed to act exactly like real people.

In the end, any solutions have to be nontechnical. We have to recognize the limitations of online political conversation, and again prioritize face-to-face interactions. These are harder to automate, and we know the people we’re talking with are actual people. This would be a cultural shift away from the internet and text, stepping back from social media and comment threads. Today that seems like a completely unrealistic solution.

Misinformation efforts are now common around the globe, conducted in more than 70 countries. This is the normal way to push propaganda in countries with authoritarian leanings, and it’s becoming the way to run a political campaign, for either a candidate or an issue.

Artificial personas are the future of propaganda. And while they may not be effective in tilting debate to one side or another, they easily drown out debate entirely. We don’t know the effect of that noise on democracy, only that it’ll be pernicious, and that it’s inevitable.

This essay previously appeared in TheAtlantic.com.

EDITED TO ADD: Jamie Susskind wrote a similar essay.

Technology and Policymakers

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/11/technology_and_.html

Technologists and policymakers largely inhabit two separate worlds. It’s an old problem, one that the British scientist CP Snow identified in a 1959 essay entitled The Two Cultures. He called them sciences and humanities, and pointed to the split as a major hindrance to solving the world’s problems. The essay was influential — but 60 years later, nothing has changed.

When Snow was writing, the two cultures theory was largely an interesting societal observation. Today, it’s a crisis. Technology is now deeply intertwined with policy. We’re building complex socio-technical systems at all levels of our society. Software constrains behavior with an efficiency that no law can match. It’s all changing fast; technology is literally creating the world we all live in, and policymakers can’t keep up. Getting it wrong has become increasingly catastrophic. Surviving the future depends in bringing technologists and policymakers together.

Consider artificial intelligence (AI). This technology has the potential to augment human decision-making, eventually replacing notoriously subjective human processes with something fairer, more consistent, faster and more scalable. But it also has the potential to entrench bias and codify inequity, and to act in ways that are unexplainable and undesirable. It can be hacked in new ways, giving attackers from criminals and nation states new capabilities to disrupt and harm. How do we avoid the pitfalls of AI while benefiting from its promise? Or, more specifically, where and how should government step in and regulate what is largely a market-driven industry? The answer requires a deep understanding of both the policy tools available to modern society and the technologies of AI.

But AI is just one of many technological areas that needs policy oversight. We also need to tackle the increasingly critical cybersecurity vulnerabilities in our infrastructure. We need to understand both the role of social media platforms in disseminating politically divisive content, and what technology can and cannot to do mitigate its harm. We need policy around the rapidly advancing technologies of bioengineering, such as genome editing and synthetic biology, lest advances cause problems for our species and planet. We’re barely keeping up with regulations on food and water safety — let alone energy policy and climate change. Robotics will soon be a common consumer technology, and we are not ready for it at all.

Addressing these issues will require policymakers and technologists to work together from the ground up. We need to create an environment where technologists get involved in public policy – where there is a viable career path for what has come to be called “public-interest technologists.”

The concept isn’t new, even if the phrase is. There are already professionals who straddle the worlds of technology and policy. They come from the social sciences and from computer science. They work in data science, or tech policy, or public-focused computer science. They worked in Bush and Obama’s White House, or in academia and NGOs. The problem is that there are too few of them; they are all exceptions and they are all exceptional. We need to find them, support them, and scale up whatever the process is that creates them.

There are two aspects to creating a scalable career path for public-interest technologists, and you can think of them as the problems of supply and demand. In the long term, supply will almost certainly be the bigger problem. There simply aren’t enough technologists who want to get involved in public policy. This will only become more critical as technology further permeates our society. We can’t begin to calculate the number of them that our society will need in the coming years and decades.

Fixing this supply problem requires changes in educational curricula, from childhood through college and beyond. Science and technology programs need to include mandatory courses in ethics, social science, policy and human-centered design. We need joint degree programs to provide even more integrated curricula. We need ways to involve people from a variety of backgrounds and capabilities. We need to foster opportunities for public-interest tech work on the side, as part of their more traditional jobs, or for a few years during their more conventional careers during designed sabbaticals or fellowships. Public service needs to be part of an academic career. We need to create, nurture and compensate people who aren’t entirely technologists or policymakers, but instead an amalgamation of the two. Public-interest technology needs to be a respected career choice, even if it will never pay what a technologist can make at a tech firm.

But while the supply side is the harder problem, the demand side is the more immediate problem. Right now, there aren’t enough places to go for scientists or technologists who want to do public policy work, and the ones that exist tend to be underfunded and in environments where technologists are unappreciated. There aren’t enough positions on legislative staffs, in government agencies, at NGOs or in the press. There aren’t enough teaching positions and fellowships at colleges and universities. There aren’t enough policy-focused technological projects. In short, not enough policymakers realize that they need scientists and technologists — preferably those with some policy training — as part of their teams.

To make effective tech policy, policymakers need to better understand technology. For some reason, ignorance about technology isn’t seen as a deficiency among our elected officials, and this is a problem. It is no longer okay to not understand how the internet, machine learning — or any other core technologies — work.

This doesn’t mean policymakers need to become tech experts. We have long expected our elected officials to regulate highly specialized areas of which they have little understanding. It’s been manageable because those elected officials have people on their staff who do understand those areas, or because they trust other elected officials who do. Policymakers need to realize that they need technologists on their policy teams, and to accept well-established scientific findings as fact. It is also no longer okay to discount technological expertise merely because it contradicts your political biases.

The evolution of public health policy serves as an instructive model. Health policy is a field that includes both policy experts who know a lot about the science and keep abreast of health research, and biologists and medical researchers who work closely with policymakers. Health policy is often a specialization at policy schools. We live in a world where the importance of vaccines is widely accepted and well-understood by policymakers, and is written into policy. Our policies on global pandemics are informed by medical experts. This serves society well, but it wasn’t always this way. Health policy was not always part of public policy. People lived through a lot of terrible health crises before policymakers figured out how to actually talk and listen to medical experts. Today we are facing a similar situation with technology.

Another parallel is public-interest law. Lawyers work in all parts of government and in many non-governmental organizations, crafting policy or just lawyering in the public interest. Every attorney at a major law firm is expected to devote some time to public-interest cases; it’s considered part of a well-rounded career. No law firm looks askance at an attorney who takes two years out of his career to work in a public-interest capacity. A tech career needs to look more like that.

In his book Future Politics, Jamie Susskind writes: “Politics in the twentieth century was dominated by a central question: how much of our collective life should be determined by the state, and what should be left to the market and civil society? For the generation now approaching political maturity, the debate will be different: to what extent should our lives be directed and controlled by powerful digital systems — and on what terms?”

I teach cybersecurity policy at the Harvard Kennedy School of Government. Because that question is fundamentally one of economics — and because my institution is a product of both the 20th century and that question — its faculty is largely staffed by economists. But because today’s question is a different one, the institution is now hiring policy-focused technologists like me.

If we’re honest with ourselves, it was never okay for technology to be separate from policy. But today, amid what we’re starting to call the Fourth Industrial Revolution, the separation is much more dangerous. We need policymakers to recognize this danger, and to welcome a new generation of technologists from every persuasion to help solve the socio-technical policy problems of the 21st century. We need to create ways to speak tech to power — and power needs to open the door and let technologists in.

This essay previously appeared on the World Economic Forum blog.

I Have a New Book: We Have Root

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/10/i_have_a_new_bo.html

I just published my third collection of essays: We Have Root. This book covers essays from 2013 to 2017. (The first two are Schneier on Security and Carry On.)

There is nothing in this book is that is not available for free on my website; but if you’d like these essays in an easy-to-carry paperback book format, you can order a signed copy here. External vendor links, including for ebook versions, here.

Supply-Chain Security and Trust

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/09/supply-chain_se_1.html

The United States government’s continuing disagreement with the Chinese company Huawei underscores a much larger problem with computer technologies in general: We have no choice but to trust them completely, and it’s impossible to verify that they’re trustworthy. Solving this problem ­ which is increasingly a national security issue ­ will require us to both make major policy changes and invent new technologies.

The Huawei problem is simple to explain. The company is based in China and subject to the rules and dictates of the Chinese government. The government could require Huawei to install back doors into the 5G routers it sells abroad, allowing the government to eavesdrop on communications or ­– even worse ­– take control of the routers during wartime. Since the United States will rely on those routers for all of its communications, we become vulnerable by building our 5G backbone on Huawei equipment.

It’s obvious that we can’t trust computer equipment from a country we don’t trust, but the problem is much more pervasive than that. The computers and smartphones you use are not built in the United States. Their chips aren’t made in the United States. The engineers who design and program them come from over a hundred countries. Thousands of people have the opportunity, acting alone, to slip a back door into the final product.

There’s more. Open-source software packages are increasingly targeted by groups installing back doors. Fake apps in the Google Play store illustrate vulnerabilities in our software distribution systems. The NotPetya worm was distributed by a fraudulent update to a popular Ukranian accounting package, illustrating vulnerabilities in our update systems. Hardware chips can be back-doored at the point of fabrication, even if the design is secure. The National Security Agency exploited the shipping process to subvert Cisco routers intended for the Syrian telephone company. The overall problem is that of supply-chain security, because every part of the supply chain can be attacked.

And while nation-state threats like China and Huawei ­– or Russia and the antivirus company Kaspersky a couple of years earlier ­– make the news, many of the vulnerabilities I described above are being exploited by cybercriminals.

Policy solutions involve forcing companies to open their technical details to inspection, including the source code of their products and the designs of their hardware. Huawei and Kaspersky have offered this sort of openness as a way to demonstrate that they are trustworthy. This is not a worthless gesture, and it helps, but it’s not nearly enough. Too many back doors can evade this kind of inspection.

Technical solutions fall into two basic categories, both currently beyond our reach. One is to improve the technical inspection processes for products whose designers provide source code and hardware design specifications, and for products that arrive without any transparency information at all. In both cases, we want to verify that the end product is secure and free of back doors. Sometimes we can do this for some classes of back doors: We can inspect source code ­ this is how a Linux back door was discovered and removed in 2003 ­ or the hardware design, which becomes a cleverness battle between attacker and defender.

This is an area that needs more research. Today, the advantage goes to the attacker. It’s hard to ensure that the hardware and software you examine is the same as what you get, and it’s too easy to create back doors that slip past inspection. And while we can find and correct some of these supply-chain attacks, we won’t find them all. It’s a needle-in-a-haystack problem, except we don’t know what a needle looks like. We need technologies, possibly based on artificial intelligence, that can inspect systems more thoroughly and faster than humans can do. We need them quickly.

The other solution is to build a secure system, even though any of its parts can be subverted. This is what the former Deputy Director of National Intelligence Sue Gordon meant in April when she said about 5G, “You have to presume a dirty network.” Or more precisely, can we solve this by building trustworthy systems out of untrustworthy parts?

It sounds ridiculous on its face, but the Internet itself was a solution to a similar problem: a reliable network built out of unreliable parts. This was the result of decades of research. That research continues today, and it’s how we can have highly resilient distributed systems like Google’s network even though none of the individual components are particularly good. It’s also the philosophy behind much of the cybersecurity industry today: systems watching one another, looking for vulnerabilities and signs of attack.

Security is a lot harder than reliability. We don’t even really know how to build secure systems out of secure parts, let alone out of parts and processes that we can’t trust and that are almost certainly being subverted by governments and criminals around the world. Current security technologies are nowhere near good enough, though, to defend against these increasingly sophisticated attacks. So while this is an important part of the solution, and something we need to focus research on, it’s not going to solve our near-term problems.

At the same time, all of these problems are getting worse as computers and networks become more critical to personal and national security. The value of 5G isn’t for you to watch videos faster; it’s for things talking to things without bothering you. These things ­– cars, appliances, power plants, smart cities –­ increasingly affect the world in a direct physical manner. They’re increasingly autonomous, using A.I. and other technologies to make decisions without human intervention. The risk from Chinese back doors into our networks and computers isn’t that their government will listen in on our conversations; it’s that they’ll turn the power off or make all the cars crash into one another.

All of this doesn’t leave us with many options for today’s supply-chain problems. We still have to presume a dirty network ­– as well as back-doored computers and phones — and we can clean up only a fraction of the vulnerabilities. Citing the lack of non-Chinese alternatives for some of the communications hardware, already some are calling to abandon attempts to secure 5G from Chinese back doors and work on having secure American or European alternatives for 6G networks. It’s not nearly enough to solve the problem, but it’s a start.

Perhaps these half-solutions are the best we can do. Live with the problem today, and accelerate research to solve the problem for the future. These are research projects on a par with the Internet itself. They need government funding, like the Internet itself. And, also like the Internet, they’re critical to national security.

Critically, these systems must be as secure as we can make them. As former FCC Commissioner Tom Wheeler has explained, there’s a lot more to securing 5G than keeping Chinese equipment out of the network. This means we have to give up the fantasy that law enforcement can have back doors to aid criminal investigations without also weakening these systems. The world uses one network, and there can only be one answer: Either everyone gets to spy, or no one gets to spy. And as these systems become more critical to national security, a network secure from all eavesdroppers becomes more important.

This essay previously appeared in the New York Times.

On Chinese "Spy Trains"

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/09/on_chinese_spy_.html

The trade war with China has reached a new industry: subway cars. Congress is considering legislation that would prevent the world’s largest train maker, the Chinese-owned CRRC Corporation, from competing on new contracts in the United States.

Part of the reasoning behind this legislation is economic, and stems from worries about Chinese industries undercutting the competition and dominating key global industries. But another part involves fears about national security. News articles talk about “spy trains,” and the possibility that the train cars might surreptitiously monitor their passengers’ faces, movements, conversations or phone calls.

This is a complicated topic. There is definitely a national security risk in buying computer infrastructure from a country you don’t trust. That’s why there is so much worry about Chinese-made equipment for the new 5G wireless networks.

It’s also why the United States has blocked the cybersecurity company Kaspersky from selling its Russian-made antivirus products to US government agencies. Meanwhile, the chairman of China’s technology giant Huawei has pointed to NSA spying disclosed by Edward Snowden as a reason to mistrust US technology companies.

The reason these threats are so real is that it’s not difficult to hide surveillance or control infrastructure in computer components, and if they’re not turned on, they’re very difficult to find.

Like every other piece of modern machinery, modern train cars are filled with computers, and while it’s certainly possible to produce a subway car with enough surveillance apparatus to turn it into a “spy train,” in practice it doesn’t make much sense. The risk of discovery is too great, and the payoff would be too low. Like the United States, China is more likely to try to get data from the US communications infrastructure, or from the large Internet companies that already collect data on our every move as part of their business model.

While it’s unlikely that China would bother spying on commuters using subway cars, it would be much less surprising if a tech company offered free Internet on subways in exchange for surveillance and data collection. Or if the NSA used those corporate systems for their own surveillance purposes (just as the agency has spied on in-flight cell phone calls, according to an investigation by the Intercept and Le Monde, citing documents provided by Edward Snowden). That’s an easier, and more fruitful, attack path.

We have credible reports that the Chinese hacked Gmail around 2010, and there are ongoing concerns about both censorship and surveillance by the Chinese social-networking company TikTok. (TikTok’s parent company has told the Washington Post that the app doesn’t send American users’ info back to Beijing, and that the Chinese government does not influence the app’s use in the United States.)

Even so, these examples illustrate an important point: there’s no escaping the technology of inevitable surveillance. You have little choice but to rely on the companies that build your computers and write your software, whether in your smartphones, your 5G wireless infrastructure, or your subway cars. And those systems are so complicated that they can be secretly programmed to operate against your interests.

Last year, Le Monde reported that the Chinese government bugged the computer network of the headquarters of the African Union in Addis Ababa. China had built and outfitted the organization’s new headquarters as a foreign aid gift, reportedly secretly configuring the network to send copies of confidential data to Shanghai every night between 2012 and 2017. China denied having done so, of course.

If there’s any lesson from all of this, it’s that everybody spies using the Internet. The United States does it. Our allies do it. Our enemies do it. Many countries do it to each other, with their success largely dependent on how sophisticated their tech industries are.

China dominates the subway car manufacturing industry because of its low prices­ — the same reason it dominates the 5G hardware industry. Whether these low prices are because the companies are more efficient than their competitors or because they’re being unfairly subsidized by the Chinese government is a matter to be determined at trade negotiations.

Finally, Americans must understand that higher prices are an inevitable result of banning cheaper tech products from China.

We might willingly pay the higher prices because we want domestic control of our telecommunications infrastructure. We might willingly pay more because of some protectionist belief that global trade is somehow bad. But we need to make these decisions to protect ourselves deliberately and rationally, recognizing both the risks and the costs. And while I’m worried about our 5G infrastructure built using Chinese hardware, I’m not worried about our subway cars.

This essay originally appeared on CNN.com.

EDITED TO ADD: I had a lot of trouble with CNN’s legal department with this essay. They were very reluctant to call out the US and its allies for similar behavior, and spent a lot more time adding caveats to statements that I didn’t think needed them. They wouldn’t let me link to this Intercept article talking about US, French, and German infiltration of supply chains, or even the NSA document from the Snowden archives that proved the statements.

Influence Operations Kill Chain

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/08/influence_opera.html

Influence operations are elusive to define. The Rand Corp.’s definition is as good as any: “the collection of tactical information about an adversary as well as the dissemination of propaganda in pursuit of a competitive advantage over an opponent.” Basically, we know it when we see it, from bots controlled by the Russian Internet Research Agency to Saudi attempts to plant fake stories and manipulate political debate. These operations have been run by Iran against the United States, Russia against Ukraine, China against Taiwan, and probably lots more besides.

Since the 2016 US presidential election, there have been an endless series of ideas about how countries can defend themselves. It’s time to pull those together into a comprehensive approach to defending the public sphere and the institutions of democracy.

Influence operations don’t come out of nowhere. They exploit a series of predictable weaknesses — and fixing those holes should be the first step in fighting them. In cybersecurity, this is known as a “kill chain.” That can work in fighting influence operations, too­ — laying out the steps of an attack and building the taxonomy of countermeasures.

In an exploratory blog post, I first laid out a straw man information operations kill chain. I started with the seven commandments, or steps, laid out in a 2018 New York Times opinion video series on “Operation Infektion,” a 1980s Russian disinformation campaign. The information landscape has changed since the 1980s, and these operations have changed as well. Based on my own research and feedback from that initial attempt, I have modified those steps to bring them into the present day. I have also changed the name from “information operations” to “influence operations,” because the former is traditionally defined by the US Department of Defense in ways that don’t really suit these sorts of attacks.

Step 1: Find the cracks in the fabric of society­ — the social, demographic, economic, and ethnic divisions. For campaigns that just try to weaken collective trust in government’s institutions, lots of cracks will do. But for influence operations that are more directly focused on a particular policy outcome, only those related to that issue will be effective.

Countermeasures: There will always be open disagreements in a democratic society, but one defense is to shore up the institutions that make that society possible. Elsewhere I have written about the “common political knowledge” necessary for democracies to function. That shared knowledge has to be strengthened, thereby making it harder to exploit the inevitable cracks. It needs to be made unacceptable — or at least costly — for domestic actors to use these same disinformation techniques in their own rhetoric and political maneuvering, and to highlight and encourage cooperation when politicians honestly work across party lines. The public must learn to become reflexively suspicious of information that makes them angry at fellow citizens. These cracks can’t be entirely sealed, as they emerge from the diversity that makes democracies strong, but they can be made harder to exploit. Much of the work in “norms” falls here, although this is essentially an unfixable problem. This makes the countermeasures in the later steps even more important.

Step 2: Build audiences, either by directly controlling a platform (like RT) or by cultivating relationships with people who will be receptive to those narratives. In 2016, this consisted of creating social media accounts run either by human operatives or automatically by bots, making them seem legitimate, gathering followers. In the years following, this has gotten subtler. As social media companies have gotten better at deleting these accounts, two separate tactics have emerged. The first is microtargeting, where influence accounts join existing social circles and only engage with a few different people. The other is influencer influencing, where these accounts only try to affect a few proxies (see step 6) — either journalists or other influencers — who can carry their message for them.

Countermeasures: This is where social media companies have made all the difference. By allowing groups of like-minded people to find and talk to each other, these companies have given propagandists the ability to find audiences who are receptive to their messages. Social media companies need to detect and delete accounts belonging to propagandists as well as bots and groups run by those propagandists. Troll farms exhibit particular behaviors that the platforms need to be able to recognize. It would be best to delete accounts early, before those accounts have the time to establish themselves.

This might involve normally competitive companies working together, since operations and account names often cross platforms, and cross-platform visibility is an important tool for identifying them. Taking down accounts as early as possible is important, because it takes time to establish the legitimacy and reach of any one account. The NSA and US Cyber Command worked with the FBI and social media companies to take down Russian propaganda accounts during the 2018 midterm elections. It may be necessary to pass laws requiring Internet companies to do this. While many social networking companies have reversed their “we don’t care” attitudes since the 2016 election, there’s no guarantee that they will continue to remove these accounts — especially since their profits depend on engagement and not accuracy.

Step 3: Seed distortion by creating alternative narratives. In the 1980s, this was a single “big lie,” but today it is more about many contradictory alternative truths — a “firehose of falsehood” — that distort the political debate. These can be fake or heavily slanted news stories, extremist blog posts, fake stories on real-looking websites, deepfake videos, and so on.

Countermeasures: Fake news and propaganda are viruses; they spread through otherwise healthy populations. Fake news has to be identified and labeled as such by social media companies and others, including recognizing and identifying manipulated videos known as deepfakes. Facebook is already making moves in this direction. Educators need to teach better digital literacy, as Finland is doing. All of this will help people recognize propaganda campaigns when they occur, so they can inoculate themselves against their effects. This alone cannot solve the problem, as much sharing of fake news is about social signaling, and those who share it care more about how it demonstrates their core beliefs than whether or not it is true. Still, it is part of the solution.

Step 4: Wrap those narratives in kernels of truth. A core of fact makes falsehoods more believable and helps them spread. Releasing stolen emails from Hillary Clinton’s campaign chairman John Podesta and the Democratic National Committee, or documents from Emmanuel Macron’s campaign in France, were both an example of that kernel of truth. Releasing stolen emails with a few deliberate falsehoods embedded among them is an even more effective tactic.

Countermeasures: Defenses involve exposing the untruths and distortions, but this is also complicated to put into practice. Fake news sows confusion just by being there. Psychologists have demonstrated that an inadvertent effect of debunking a piece of fake news is to amplify the message of that debunked story. Hence, it is essential to replace the fake news with accurate narratives that counter the propaganda. That kernel of truth is part of a larger true narrative. The media needs to learn skepticism about the chain of information and to exercise caution in how they approach debunked stories.

Step 5: Conceal your hand. Make it seem as if the stories came from somewhere else.

Countermeasures: Here the answer is attribution, attribution, attribution. The quicker an influence operation can be pinned on an attacker, the easier it is to defend against it. This will require efforts by both the social media platforms and the intelligence community, not just to detect influence operations and expose them but also to be able to attribute attacks. Social media companies need to be more transparent about how their algorithms work and make source publications more obvious for online articles. Even small measures like the Honest Ads Act, requiring transparency in online political ads, will help. Where companies lack business incentives to do this, regulation will be the only answer.

Step 6: Cultivate proxies who believe and amplify the narratives. Traditionally, these people have been called “useful idiots.” Encourage them to take action outside of the Internet, like holding political rallies, and to adopt positions even more extreme than they would otherwise.

Countermeasures: We can mitigate the influence of people who disseminate harmful information, even if they are unaware they are amplifying deliberate propaganda. This does not mean that the government needs to regulate speech; corporate platforms already employ a variety of systems to amplify and diminish particular speakers and messages. Additionally, the antidote to the ignorant people who repeat and amplify propaganda messages is other influencers who respond with the truth — in the words of one report, we must “make the truth louder.” Of course, there will always be true believers for whom no amount of fact-checking or counter-speech will suffice; this is not intended for them. Focus instead on persuading the persuadable.

Step 7: Deny involvement in the propaganda campaign, even if the truth is obvious. Although since one major goal is to convince people that nothing can be trusted, rumors of involvement can be beneficial. The first was Russia’s tactic during the 2016 US presidential election; it employed the second during the 2018 midterm elections.

Countermeasures: When attack attribution relies on secret evidence, it is easy for the attacker to deny involvement. Public attribution of information attacks must be accompanied by convincing evidence. This will be difficult when attribution involves classified intelligence information, but there is no alternative. Trusting the government without evidence, as the NSA’s Rob Joyce recommended in a 2016 talk, is not enough. Governments will have to disclose.

Step 8: Play the long game. Strive for long-term impact over immediate effects. Engage in multiple operations; most won’t be successful, but some will.

Countermeasures: Counterattacks can disrupt the attacker’s ability to maintain influence operations, as US Cyber Command did during the 2018 midterm elections. The NSA’s new policy of “persistent engagement” (see the article by, and interview with, US Cyber Command Commander Paul Nakasone here) is a strategy to achieve this. So are targeted sanctions and indicting individuals involved in these operations. While there is little hope of bringing them to the United States to stand trial, the possibility of not being able to travel internationally for fear of being arrested will lead some people to refuse to do this kind of work. More generally, we need to better encourage both politicians and social media companies to think beyond the next election cycle or quarterly earnings report.

Permeating all of this is the importance of deterrence. Deterring them will require a different theory. It will require, as the political scientist Henry Farrell and I have postulated, thinking of democracy itself as an information system and understanding “Democracy’s Dilemma“: how the very tools of a free and open society can be subverted to attack that society. We need to adjust our theories of deterrence to the realities of the information age and the democratization of attackers. If we can mitigate the effectiveness of influence operations, if we can publicly attribute, if we can respond either diplomatically or otherwise — we can deter these attacks from nation-states.

None of these defensive actions is sufficient on its own. Steps overlap and in some cases can be skipped. Steps can be conducted simultaneously or out of order. A single operation can span multiple targets or be an amalgamation of multiple attacks by multiple actors. Unlike a cyberattack, disrupting will require more than disrupting any particular step. It will require a coordinated effort between government, Internet platforms, the media, and others.

Also, this model is not static, of course. Influence operations have already evolved since the 2016 election and will continue to evolve over time — especially as countermeasures are deployed and attackers figure out how to evade them. We need to be prepared for wholly different kinds of influencer operations during the 2020 US presidential election. The goal of this kill chain is to be general enough to encompass a panoply of tactics but specific enough to illuminate countermeasures. But even if this particular model doesn’t fit every influence operation, it’s important to start somewhere.

Others have worked on similar ideas. Anthony Soules, a former NSA employee who now leads cybersecurity strategy for Amgen, presented this concept at a private event. Clint Watts of the Alliance for Securing Democracy is thinking along these lines as well. The Credibility Coalition’s Misinfosec Working Group proposed a “misinformation pyramid.” The US Justice Department developed a “Malign Foreign Influence Campaign Cycle,” with associated countermeasures.

The threat from influence operations is real and important, and it deserves more study. At the same time, there’s no reason to panic. Just as overly optimistic technologists were wrong that the Internet was the single technology that was going to overthrow dictators and liberate the planet, so pessimists are also probably wrong that it is going to empower dictators and destroy democracy. If we deploy countermeasures across the entire kill chain, we can defend ourselves from these attacks.

But Russian interference in the 2016 presidential election shows not just that such actions are possible but also that they’re surprisingly inexpensive to run. As these tactics continue to be democratized, more people will attempt them. And as more people, and multiple parties, conduct influence operations, they will increasingly be seen as how the game of politics is played in the information age. This means that the line will increasingly blur between influence operations and politics as usual, and that domestic influencers will be using them as part of campaigning. Defending democracy against foreign influence also necessitates making our own political debate healthier.

This essay previously appeared in Foreign Policy.

Attorney General William Barr on Encryption Policy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/07/attorney_genera_1.html

Yesterday, Attorney General William Barr gave a major speech on encryption policy — what is commonly known as “going dark.” Speaking at Fordham University in New York, he admitted that adding backdoors decreases security but that it is worth it.

Some hold this view dogmatically, claiming that it is technologically impossible to provide lawful access without weakening security against unlawful access. But, in the world of cybersecurity, we do not deal in absolute guarantees but in relative risks. All systems fall short of optimality and have some residual risk of vulnerability a point which the tech community acknowledges when they propose that law enforcement can satisfy its requirements by exploiting vulnerabilities in their products. The real question is whether the residual risk of vulnerability resulting from incorporating a lawful access mechanism is materially greater than those already in the unmodified product. The Department does not believe this can be demonstrated.

Moreover, even if there was, in theory, a slight risk differential, its significance should not be judged solely by the extent to which it falls short of theoretical optimality. Particularly with respect to encryption marketed to consumers, the significance of the risk should be assessed based on its practical effect on consumer cybersecurity, as well as its relation to the net risks that offering the product poses for society. After all, we are not talking about protecting the Nation’s nuclear launch codes. Nor are we necessarily talking about the customized encryption used by large business enterprises to protect their operations. We are talking about consumer products and services such as messaging, smart phones, e-mail, and voice and data applications. If one already has an effective level of security say, by way of illustration, one that protects against 99 percent of foreseeable threats is it reasonable to incur massive further costs to move slightly closer to optimality and attain a 99.5 percent level of protection? A company would not make that expenditure; nor should society. Here, some argue that, to achieve at best a slight incremental improvement in security, it is worth imposing a massive cost on society in the form of degraded safety. This is untenable. If the choice is between a world where we can achieve a 99 percent assurance against cyber threats to consumers, while still providing law enforcement 80 percent of the access it might seek; or a world, on the other hand, where we have boosted our cybersecurity to 99.5 percent but at a cost reducing law enforcements [sic] access to zero percent the choice for society is clear.

I think this is a major change in government position. Previously, the FBI, the Justice Department and so on had claimed that backdoors for law enforcement could be added without any loss of security. They maintained that technologists just need to figure out how: ­an approach we have derisively named “nerd harder.”

With this change, we can finally have a sensible policy conversation. Yes, adding a backdoor increases our collective security because it allows law enforcement to eavesdrop on the bad guys. But adding that backdoor also decreases our collective security because the bad guys can eavesdrop on everyone. This is exactly the policy debate we should be having­not the fake one about whether or not we can have both security and surveillance.

Barr makes the point that this is about “consumer cybersecurity,” and not “nuclear launch codes.” This is true, but ignores the huge amount of national security-related communications between those two poles. The same consumer communications and computing devices are used by our lawmakers, CEOs, legislators, law enforcement officers, nuclear power plant operators, election officials and so on. There’s no longer a difference between consumer tech and government tech — it’s all the same tech.

Barr also says:

Further, the burden is not as onerous as some make it out to be. I served for many years as the general counsel of a large telecommunications concern. During my tenure, we dealt with these issues and lived through the passage and implementation of CALEA the Communications Assistance for Law Enforcement Act. CALEA imposes a statutory duty on telecommunications carriers to maintain the capability to provide lawful access to communications over their facilities. Companies bear the cost of compliance but have some flexibility in how they achieve it, and the system has by and large worked. I therefore reserve a heavy dose of skepticism for those who claim that maintaining a mechanism for lawful access would impose an unreasonable burden on tech firms especially the big ones. It is absurd to think that we would preserve lawful access by mandating that physical telecommunications facilities be accessible to law enforcement for the purpose of obtaining content, while allowing tech providers to block law enforcement from obtaining that very content.

That telecommunications company was GTE­which became Verizon. Barr conveniently ignores that CALEA-enabled phone switches were used to spy on government officials in Greece in 2003 — which seems to have been an NSA operation — and on a variety of people in Italy in 2006. Moreover, in 2012 every CALEA-enabled switch sold to the Defense Department had security vulnerabilities. (I wrote about all this, and more, in 2013.)

The final thing I noticed about the speech is that is it not about iPhones and data at rest. It is about communications: ­data in transit. The “going dark” debate has bounced back and forth between those two aspects for decades. It seems to be bouncing once again.

I hope that Barr’s latest speech signals that we can finally move on from the fake security vs. privacy debate, and to the real security vs. security debate. I know where I stand on that: As computers continue to permeate every aspect of our lives, society, and critical infrastructure, it is much more important to ensure that they are secure from everybody — even at the cost of law-enforcement access — than it is to allow access at the cost of security. Barr is wrong, it kind of is like these systems are protecting nuclear launch codes.

This essay previously appeared on Lawfare.com.

EDITED TO ADD: More news articles.

EDITED TO ADD (7/28): Gen. Hayden comments.

EDITED TO ADD (7/30): Good response by Robert Graham.

Fake News and Pandemics

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/fake_news_and_p.html

When the next pandemic strikes, we’ll be fighting it on two fronts. The first is the one you immediately think about: understanding the disease, researching a cure and inoculating the population. The second is new, and one you might not have thought much about: fighting the deluge of rumors, misinformation and flat-out lies that will appear on the internet.

The second battle will be like the Russian disinformation campaigns during the 2016 presidential election, only with the addition of a deadly health crisis and possibly without a malicious government actor. But while the two problems — misinformation affecting democracy and misinformation affecting public health — will have similar solutions, the latter is much less political. If we work to solve the pandemic disinformation problem, any solutions are likely to also be applicable to the democracy one.

Pandemics are part of our future. They might be like the 1968 Hong Kong flu, which killed a million people, or the 1918 Spanish flu, which killed over 40 million. Yes, modern medicine makes pandemics less likely and less deadly. But global travel and trade, increased population density, decreased wildlife habitats, and increased animal farming to satisfy a growing and more affluent population have made them more likely. Experts agree that it’s not a matter of if — it’s only a matter of when.

When the next pandemic strikes, accurate information will be just as important as effective treatments. We saw this in 2014, when the Nigerian government managed to contain a subcontinentwide Ebola epidemic to just 20 infections and eight fatalities. Part of that success was because of the ways officials communicated health information to all Nigerians, using government-sponsored videos, social media campaigns and international experts. Without that, the death toll in Lagos, a city of 21 million people, would have probably been greater than the 11,000 the rest of the continent experienced.

There’s every reason to expect misinformation to be rampant during a pandemic. In the early hours and days, information will be scant and rumors will abound. Most of us are not health professionals or scientists. We won’t be able to tell fact from fiction. Even worse, we’ll be scared. Our brains work differently when we are scared, and they latch on to whatever makes us feel safer — even if it’s not true.

Rumors and misinformation could easily overwhelm legitimate news channels, as people share tweets, images and videos. Much of it will be well-intentioned but wrong — like the misinformation spread by the anti-vaccination community today ­– but some of it may be malicious. In the 1980s, the KGB ran a sophisticated disinformation campaign ­– Operation Infektion ­– to spread the rumor that HIV/AIDS was a result of an American biological weapon gone awry. It’s reasonable to assume some group or country would deliberately spread intentional lies in an attempt to increase death and chaos.

It’s not just misinformation about which treatments work (and are safe), and which treatments don’t work (and are unsafe). Misinformation can affect society’s ability to deal with a pandemic at many different levels. Right now, Ebola relief efforts in the Democratic Republic of Congo are being stymied by mistrust of health workers and government officials.

It doesn’t take much to imagine how this can lead to disaster. Jay Walker, curator of the TEDMED conferences, laid out some of the possibilities in a 2016 essay: people overwhelming and even looting pharmacies trying to get some drug that is irrelevant or nonexistent, people needlessly fleeing cities and leaving them paralyzed, health workers not showing up for work, truck drivers and other essential people being afraid to enter infected areas, official sites like CDC.gov being hacked and discredited. This kind of thing can magnify the health effects of a pandemic many times over, and in extreme cases could lead to a total societal collapse.

This is going to be something that government health organizations, medical professionals, social media companies and the traditional media are going to have to work out together. There isn’t any single solution; it will require many different interventions that will all need to work together. The interventions will look a lot like what we’re already talking about with regard to government-run and other information influence campaigns that target our democratic processes: methods of visibly identifying false stories, the identification and deletion of fake posts and accounts, ways to promote official and accurate news, and so on. At the scale these are needed, they will have to be done automatically and in real time.

Since the 2016 presidential election, we have been talking about propaganda campaigns, and about how social media amplifies fake news and allows damaging messages to spread easily. It’s a hard discussion to have in today’s hyperpolarized political climate. After any election, the winning side has every incentive to downplay the role of fake news.

But pandemics are different; there’s no political constituency in favor of people dying because of misinformation. Google doesn’t want the results of peoples’ well-intentioned searches to lead to fatalities. Facebook and Twitter don’t want people on their platforms sharing misinformation that will result in either individual or mass deaths. Focusing on pandemics gives us an apolitical way to collectively approach the general problem of misinformation and fake news. And any solutions for pandemics are likely to also be applicable to the more general ­– and more political ­– problems.

Pandemics are inevitable. Bioterror is already possible, and will only get easier as the requisite technologies become cheaper and more common. We’re experiencing the largest measles outbreak in 25 years thanks to the anti-vaccination movement, which has hijacked social media to amplify its messages; we seem unable to beat back the disinformation and pseudoscience surrounding the vaccine. Those same forces will dramatically increase death and social upheaval in the event of a pandemic.

Let the Russian propaganda attacks on the 2016 election serve as a wake-up call for this and other threats. We need to solve the problem of misinformation during pandemics together –­ governments and industries in collaboration with medical officials, all across the world ­– before there’s a crisis. And the solutions will also help us shore up our democracy in the process.

This essay previously appeared in the New York Times.

Data, Surveillance, and the AI Arms Race

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/data_surveillan.html

According to foreign policy experts and the defense establishment, the United States is caught in an artificial intelligence arms race with China — one with serious implications for national security. The conventional version of this story suggests that the United States is at a disadvantage because of self-imposed restraints on the collection of data and the privacy of its citizens, while China, an unrestrained surveillance state, is at an advantage. In this vision, the data that China collects will be fed into its systems, leading to more powerful AI with capabilities we can only imagine today. Since Western countries can’t or won’t reap such a comprehensive harvest of data from their citizens, China will win the AI arms race and dominate the next century.

This idea makes for a compelling narrative, especially for those trying to justify surveillance — whether government- or corporate-run. But it ignores some fundamental realities about how AI works and how AI research is conducted.

Thanks to advances in machine learning, AI has flipped from theoretical to practical in recent years, and successes dominate public understanding of how it works. Machine learning systems can now diagnose pneumonia from X-rays, play the games of go and poker, and read human lips, all better than humans. They’re increasingly watching surveillance video. They are at the core of self-driving car technology and are playing roles in both intelligence-gathering and military operations. These systems monitor our networks to detect intrusions and look for spam and malware in our email.

And it’s true that there are differences in the way each country collects data. The United States pioneered “surveillance capitalism,” to use the Harvard University professor Shoshana Zuboff’s term, where data about the population is collected by hundreds of large and small companies for corporate advantage — and mutually shared or sold for profit The state picks up on that data, in cases such as the Centers for Disease Control and Prevention’s use of Google search data to map epidemics and evidence shared by alleged criminals on Facebook, but it isn’t the primary user.

China, on the other hand, is far more centralized. Internet companies collect the same sort of data, but it is shared with the government, combined with government-collected data, and used for social control. Every Chinese citizen has a national ID number that is demanded by most services and allows data to easily be tied together. In the western region of Xinjiang, ubiquitous surveillance is used to oppress the Uighur ethnic minority — although at this point there is still a lot of human labor making it all work. Everyone expects that this is a test bed for the entire country.

Data is increasingly becoming a part of control for the Chinese government. While many of these plans are aspirational at the moment — there isn’t, as some have claimed, a single “social credit score,” but instead future plans to link up a wide variety of systems — data collection is universally pushed as essential to the future of Chinese AI. One executive at search firm Baidu predicted that the country’s connected population will provide them with the raw data necessary to become the world’s preeminent tech power. China’s official goal is to become the world AI leader by 2030, aided in part by all of this massive data collection and correlation.

This all sounds impressive, but turning massive databases into AI capabilities doesn’t match technological reality. Current machine learning techniques aren’t all that sophisticated. All modern AI systems follow the same basic methods. Using lots of computing power, different machine learning models are tried, altered, and tried again. These systems use a large amount of data (the training set) and an evaluation function to distinguish between those models and variations that work well and those that work less well. After trying a lot of models and variations, the system picks the one that works best. This iterative improvement continues even after the system has been fielded and is in use.

So, for example, a deep learning system trying to do facial recognition will have multiple layers (hence the notion of “deep”) trying to do different parts of the facial recognition task. One layer will try to find features in the raw data of a picture that will help find a face, such as changes in color that will indicate an edge. The next layer might try to combine these lower layers into features like shapes, looking for round shapes inside of ovals that indicate eyes on a face. The different layers will try different features and will be compared by the evaluation function until the one that is able to give the best results is found, in a process that is only slightly more refined than trial and error.

Large data sets are essential to making this work, but that doesn’t mean that more data is automatically better or that the system with the most data is automatically the best system. Train a facial recognition algorithm on a set that contains only faces of white men, and the algorithm will have trouble with any other kind of face. Use an evaluation function that is based on historical decisions, and any past bias is learned by the algorithm. For example, mortgage loan algorithms trained on historic decisions of human loan officers have been found to implement redlining. Similarly, hiring algorithms trained on historical data manifest the same sexism as human staff often have. Scientists are constantly learning about how to train machine learning systems, and while throwing a large amount of data and computing power at the problem can work, more subtle techniques are often more successful. All data isn’t created equal, and for effective machine learning, data has to be both relevant and diverse in the right ways.

Future research advances in machine learning are focused on two areas. The first is in enhancing how these systems distinguish between variations of an algorithm. As different versions of an algorithm are run over the training data, there needs to be some way of deciding which version is “better.” These evaluation functions need to balance the recognition of an improvement with not over-fitting to the particular training data. Getting functions that can automatically and accurately distinguish between two algorithms based on minor differences in the outputs is an art form that no amount of data can improve.

The second is in the machine learning algorithms themselves. While much of machine learning depends on trying different variations of an algorithm on large amounts of data to see which is most successful, the initial formulation of the algorithm is still vitally important. The way the algorithms interact, the types of variations attempted, and the mechanisms used to test and redirect the algorithms are all areas of active research. (An overview of some of this work can be found here; even trying to limit the research to 20 papers oversimplifies the work being done in the field.) None of these problems can be solved by throwing more data at the problem.

The British AI company DeepMind’s success in teaching a computer to play the Chinese board game go is illustrative. Its AlphaGo computer program became a grandmaster in two steps. First, it was fed some enormous number of human-played games. Then, the game played itself an enormous number of times, improving its own play along the way. In 2016, AlphaGo beat the grandmaster Lee Sedol four games to one.

While the training data in this case, the human-played games, was valuable, even more important was the machine learning algorithm used and the function that evaluated the relative merits of different game positions. Just one year later, DeepMind was back with a follow-on system: AlphaZero. This go-playing computer dispensed entirely with the human-played games and just learned by playing against itself over and over again. It plays like an alien. (It also became a grandmaster in chess and shogi.)

These are abstract games, so it makes sense that a more abstract training process works well. But even something as visceral as facial recognition needs more than just a huge database of identified faces in order to work successfully. It needs the ability to separate a face from the background in a two-dimensional photo or video and to recognize the same face in spite of changes in angle, lighting, or shadows. Just adding more data may help, but not nearly as much as added research into what to do with the data once we have it.

Meanwhile, foreign-policy and defense experts are talking about AI as if it were the next nuclear arms race, with the country that figures it out best or first becoming the dominant superpower for the next century. But that didn’t happen with nuclear weapons, despite research only being conducted by governments and in secret. It certainly won’t happen with AI, no matter how much data different nations or companies scoop up.

It is true that China is investing a lot of money into artificial intelligence research: The Chinese government believes this will allow it to leapfrog other countries (and companies in those countries) and become a major force in this new and transformative area of computing — and it may be right. On the other hand, much of this seems to be a wasteful boondoggle. Slapping “AI” on pretty much anything is how to get funding. The Chinese Ministry of Education, for instance, promises to produce “50 world-class AI textbooks,” with no explanation of what that means.

In the democratic world, the government is neither the leading researcher nor the leading consumer of AI technologies. AI research is much more decentralized and academic, and it is conducted primarily in the public eye. Research teams keep their training data and models proprietary but freely publish their machine learning algorithms. If you wanted to work on machine learning right now, you could download Microsoft’s Cognitive Toolkit, Google’s Tensorflow, or Facebook’s Pytorch. These aren’t toy systems; these are the state-of-the art machine learning platforms.

AI is not analogous to the big science projects of the previous century that brought us the atom bomb and the moon landing. AI is a science that can be conducted by many different groups with a variety of different resources, making it closer to computer design than the space race or nuclear competition. It doesn’t take a massive government-funded lab for AI research, nor the secrecy of the Manhattan Project. The research conducted in the open science literature will trump research done in secret because of the benefits of collaboration and the free exchange of ideas.

While the United States should certainly increase funding for AI research, it should continue to treat it as an open scientific endeavor. Surveillance is not justified by the needs of machine learning, and real progress in AI doesn’t need it.

This essay was written with Jim Waldo, and previously appeared in Foreign Policy.

Computers and Video Surveillance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/computers_and_video.html

It used to be that surveillance cameras were passive. Maybe they just recorded, and no one looked at the video unless they needed to. Maybe a bored guard watched a dozen different screens, scanning for something interesting. In either case, the video was only stored for a few days because storage was expensive.

Increasingly, none of that is true. Recent developments in video analytics — fueled by artificial intelligence techniques like machine learning — enable computers to watch and understand surveillance videos with human-like discernment. Identification technologies make it easier to automatically figure out who is in the videos. And finally, the cameras themselves have become cheaper, more ubiquitous, and much better; cameras mounted on drones can effectively watch an entire city. Computers can watch all the video without human issues like distraction, fatigue, training, or needing to be paid. The result is a level of surveillance that was impossible just a few years ago.

An ACLU report published Thursday called “the Dawn of Robot Surveillance” says AI-aided video surveillance “won’t just record us, but will also make judgments about us based on their understanding of our actions, emotions, skin color, clothing, voice, and more. These automated ‘video analytics’ technologies threaten to fundamentally change the nature of surveillance.”

Let’s take the technologies one at a time. First: video analytics. Computers are getting better at recognizing what’s going on in a video. Detecting when a person or vehicle enters a forbidden area is easy. Modern systems can alarm when someone is walking in the wrong direction — going in through an exit-only corridor, for example. They can count people or cars. They can detect when luggage is left unattended, or when previously unattended luggage is picked up and removed. They can detect when someone is loitering in an area, is lying down, or is running. Increasingly, they can detect particular actions by people. Amazon’s cashier-less stores rely on video analytics to figure out when someone picks an item off a shelf and doesn’t put it back.

More than identifying actions, video analytics allow computers to understand what’s going on in a video: They can flag people based on their clothing or behavior, identify people’s emotions through body language and behavior, and find people who are acting “unusual” based on everyone else around them. Those same Amazon in-store cameras can analyze customer sentiment. Other systems can describe what’s happening in a video scene.

Computers can also identify people. AIs are getting better at identifying people in those videos. Facial recognition technology is improving all the time, made easier by the enormous stockpile of tagged photographs we give to Facebook and other social media sites, and the photos governments collect in the process of issuing ID cards and drivers licenses. The technology already exists to automatically identify everyone a camera “sees” in real time. Even without video identification, we can be identified by the unique information continuously broadcasted by the smartphones we carry with us everywhere, or by our laptops or Bluetooth-connected devices. Police have been tracking phones for years, and this practice can now be combined with video analytics.

Once a monitoring system identifies people, their data can be combined with other data, either collected or purchased: from cell phone records, GPS surveillance history, purchasing data, and so on. Social media companies like Facebook have spent years learning about our personalities and beliefs by what we post, comment on, and “like.” This is “data inference,” and when combined with video it offers a powerful window into people’s behaviors and motivations.

Camera resolution is also improving. Gigapixel cameras as so good that they can capture individual faces and identify license places in photos taken miles away. “Wide-area surveillance” cameras can be mounted on airplanes and drones, and can operate continuously. On the ground, cameras can be hidden in street lights and other regular objects. In space, satellite cameras have also dramatically improved.

Data storage has become incredibly cheap, and cloud storage makes it all so easy. Video data can easily be saved for years, allowing computers to conduct all of this surveillance backwards in time.

In democratic countries, such surveillance is marketed as crime prevention — or counterterrorism. In countries like China, it is blatantly used to suppress political activity and for social control. In all instances, it’s being implemented without a lot of public debate by law-enforcement agencies and by corporations in public spaces they control.

This is bad, because ubiquitous surveillance will drastically change our relationship to society. We’ve never lived in this sort of world, even those of us who have lived through previous totalitarian regimes. The effects will be felt in many different areas. False positives­ — when the surveillance system gets it wrong­ — will lead to harassment and worse. Discrimination will become automated. Those who fall outside norms will be marginalized. And most importantly, the inability to live anonymously will have an enormous chilling effect on speech and behavior, which in turn will hobble society’s ability to experiment and change. A recent ACLU report discusses these harms in more depth. While it’s possible that some of this surveillance is worth the trade-offs, we as society need to deliberately and intelligently make decisions about it.

Some jurisdictions are starting to notice. Last month, San Francisco became the first city to ban facial recognition technology by police and other government agencies. A similar ban is being considered in Somerville, MA, and Oakland, CA. These are exceptions, and limited to the more liberal areas of the country.

We often believe that technological change is inevitable, and that there’s nothing we can do to stop it — or even to steer it. That’s simply not true. We’re led to believe this because we don’t often see it, understand it, or have a say in how or when it is deployed. The problem is that technologies of cameras, resolution, machine learning, and artificial intelligence are complex and specialized.

Laws like what was just passed in San Francisco won’t stop the development of these technologies, but they’re not intended to. They’re intended as pauses, so our policy making can catch up with technology. As a general rule, the US government tends to ignore technologies as they’re being developed and deployed, so as not to stifle innovation. But as the rate of technological change increases, so does the unanticipated effects on our lives. Just as we’ve been surprised by the threats to democracy caused by surveillance capitalism, AI-enabled video surveillance will have similar surprising effects. Maybe a pause in our headlong deployment of these technologies will allow us the time to discuss what kind of society we want to live in, and then enact rules to bring that kind of society about.

This essay previously appeared on Vice Motherboard.

Fraudulent Academic Papers

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/05/fraudulent_acad.html

The term “fake news” has lost much of its meaning, but it describes a real and dangerous Internet trend. Because it’s hard for many people to differentiate a real news site from a fraudulent one, they can be hoodwinked by fictitious news stories pretending to be real. The result is that otherwise reasonable people believe lies.

The trends fostering fake news are more general, though, and we need to start thinking about how it could affect different areas of our lives. In particular, I worry about how it will affect academia. In addition to fake news, I worry about fake research.

An example of this seems to have happened recently in the cryptography field. SIMON is a block cipher designed by the National Security Agency (NSA) and made public in 2013. It’s a general design optimized for hardware implementation, with a variety of block sizes and key lengths. Academic cryptanalysts have been trying to break the cipher since then, with some pretty good results, although the NSA’s specified parameters are still immune to attack. Last week, a paper appeared on the International Association for Cryptologic Research (IACR) ePrint archive purporting to demonstrate a much more effective break of SIMON, one that would affect actual implementations. The paper was sufficiently weird, the authors sufficiently unknown and the details of the attack sufficiently absent, that the editors took it down a few days later. No harm done in the end.

In recent years, there has been a push to speed up the process of disseminating research results. Instead of the laborious process of academic publication, researchers have turned to faster online publishing processes, preprint servers, and simply posting research results. The IACR ePrint archive is one of those alternatives. This has all sorts of benefits, but one of the casualties is the process of peer review. As flawed as that process is, it does help ensure the accuracy of results. (Of course, bad papers can still make it through the process. We’re still dealing with the aftermath of a flawed, and now retracted, Lancet paper linking vaccines with autism.)

Like the news business, academic publishing is subject to abuse. We can only speculate the motivations of the three people who are listed as authors on the SIMON paper, but you can easily imagine better-executed and more nefarious scenarios. In a world of competitive research, one group might publish a fake result to throw other researchers off the trail. It might be a company trying to gain an advantage over a potential competitor, or even a country trying to gain an advantage over another country.

Reverting to a slower and more accurate system isn’t the answer; the world is just moving too fast for that. We need to recognize that fictitious research results can now easily be injected into our academic publication system, and tune our skepticism meters accordingly.

This essay previously appeared on Lawfare.com.

Cybersecurity for the Public Interest

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/05/cybersecurity_f_2.html

The Crypto Wars have been waging off-and-on for a quarter-century. On one side is law enforcement, which wants to be able to break encryption, to access devices and communications of terrorists and criminals. On the other are almost every cryptographer and computer security expert, repeatedly explaining that there’s no way to provide this capability without also weakening the security of every user of those devices and communications systems.

It’s an impassioned debate, acrimonious at times, but there are real technologies that can be brought to bear on the problem: key-escrow technologies, code obfuscation technologies, and backdoors with different properties. Pervasive surveillance capitalism­ — as practiced by the Internet companies that are already spying on everyone — ­matters. So does society’s underlying security needs. There is a security benefit to giving access to law enforcement, even though it would inevitably and invariably also give that access to others. However, there is also a security benefit of having these systems protected from all attackers, including law enforcement. These benefits are mutually exclusive. Which is more important, and to what degree?

The problem is that almost no policymakers are discussing this policy issue from a technologically informed perspective, and very few technologists truly understand the policy contours of the debate. The result is both sides consistently talking past each other, and policy proposals­ — that occasionally become law­ — that are technological disasters.

This isn’t sustainable, either for this issue or any of the other policy issues surrounding Internet security. We need policymakers who understand technology, but we also need cybersecurity technologists who understand — ­and are involved in — ­policy. We need public-interest technologists.

Let’s pause at that term. The Ford Foundation defines public-interest technologists as “technology practitioners who focus on social justice, the common good, and/or the public interest.” A group of academics recently wrote that public-interest technologists are people who “study the application of technology expertise to advance the public interest, generate public benefits, or promote the public good.” Tim Berners-Lee has called them “philosophical engineers.” I think of public-interest technologists as people who combine their technological expertise with a public-interest focus: by working on tech policy, by working on a tech project with a public benefit, or by working as a traditional technologist for an organization with a public benefit. Maybe it’s not the best term­ — and I know not everyone likes it­ — but it’s a decent umbrella term that can encompass all these roles.

We need public-interest technologists in policy discussions. We need them on congressional staff, in federal agencies, at non-governmental organizations (NGOs), in academia, inside companies, and as part of the press. In our field, we need them to get involved in not only the Crypto Wars, but everywhere cybersecurity and policy touch each other: the vulnerability equities debate, election security, cryptocurrency policy, Internet of Things safety and security, big data, algorithmic fairness, adversarial machine learning, critical infrastructure, and national security. When you broaden the definition of Internet security, many additional areas fall within the intersection of cybersecurity and policy. Our particular expertise and way of looking at the world is critical for understanding a great many technological issues, such as net neutrality and the regulation of critical infrastructure. I wouldn’t want to formulate public policy about artificial intelligence and robotics without a security technologist involved.

Public-interest technology isn’t new. Many organizations are working in this area, from older organizations like EFF and EPIC to newer ones like Verified Voting and Access Now. Many academic classes and programs combine technology and public policy. My cybersecurity policy class at the Harvard Kennedy School is just one example. Media startups like The Markup are doing technology-driven journalism. There are even programs and initiatives related to public-interest technology inside for-profit corporations.

This might all seem like a lot, but it’s really not. There aren’t enough people doing it, there aren’t enough people who know it needs to be done, and there aren’t enough places to do it. We need to build a world where there is a viable career path for public-interest technologists.

There are many barriers. There’s a report titled A Pivotal Moment that includes this quote: “While we cite individual instances of visionary leadership and successful deployment of technology skill for the public interest, there was a consensus that a stubborn cycle of inadequate supply, misarticulated demand, and an inefficient marketplace stymie progress.”

That quote speaks to the three places for intervention. One: the supply side. There just isn’t enough talent to meet the eventual demand. This is especially acute in cybersecurity, which has a talent problem across the field. Public-interest technologists are a diverse and multidisciplinary group of people. Their backgrounds come from technology, policy, and law. We also need to foster diversity within public-interest technology; the populations using the technology must be represented in the groups that shape the technology. We need a variety of ways for people to engage in this sphere: ways people can do it on the side, for a couple of years between more traditional technology jobs, or as a full-time rewarding career. We need public-interest technology to be part of every core computer-science curriculum, with “clinics” at universities where students can get a taste of public-interest work. We need technology companies to give people sabbaticals to do this work, and then value what they’ve learned and done.

Two: the demand side. This is our biggest problem right now; not enough organizations understand that they need technologists doing public-interest work. We need jobs to be funded across a wide variety of NGOs. We need staff positions throughout the government: executive, legislative, and judiciary branches. President Obama’s US Digital Service should be expanded and replicated; so should Code for America. We need more press organizations that perform this kind of work.

Three: the marketplace. We need job boards, conferences, and skills exchanges­ — places where people on the supply side can learn about the demand.

Major foundations are starting to provide funding in this space: the Ford and MacArthur Foundations in particular, but others as well.

This problem in our field has an interesting parallel with the field of public-interest law. In the 1960s, there was no such thing as public-interest law. The field was deliberately created, funded by organizations like the Ford Foundation. They financed legal aid clinics at universities, so students could learn housing, discrimination, or immigration law. They funded fellowships at organizations like the ACLU and the NAACP. They created a world where public-interest law is valued, where all the partners at major law firms are expected to have done some public-interest work. Today, when the ACLU advertises for a staff attorney, paying one-third to one-tenth normal salary, it gets hundreds of applicants. Today, 20% of Harvard Law School graduates go into public-interest law, and the school has soul-searching seminars because that percentage is so low. Meanwhile, the percentage of computer-science graduates going into public-interest work is basically zero.

This is bigger than computer security. Technology now permeates society in a way it didn’t just a couple of decades ago, and governments move too slowly to take this into account. That means technologists now are relevant to all sorts of areas that they had no traditional connection to: climate change, food safety, future of work, public health, bioengineering.

More generally, technologists need to understand the policy ramifications of their work. There’s a pervasive myth in Silicon Valley that technology is politically neutral. It’s not, and I hope most people reading this today knows that. We built a world where programmers felt they had an inherent right to code the world as they saw fit. We were allowed to do this because, until recently, it didn’t matter. Now, too many issues are being decided in an unregulated capitalist environment where significant social costs are too often not taken into account.

This is where the core issues of society lie. The defining political question of the 20th century was: “What should be governed by the state, and what should be governed by the market?” This defined the difference between East and West, and the difference between political parties within countries. The defining political question of the first half of the 21st century is: “How much of our lives should be governed by technology, and under what terms?” In the last century, economists drove public policy. In this century, it will be technologists.

The future is coming faster than our current set of policy tools can deal with. The only way to fix this is to develop a new set of policy tools with the help of technologists. We need to be in all aspects of public-interest work, from informing policy to creating tools all building the future. The world needs all of our help.

This essay previously appeared in the January/February 2019 issue of IEEE Security & Privacy. I maintain a public-interest tech resources page here.

Defending Democracies Against Information Attacks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/04/defending_democ.html

To better understand influence attacks, we proposed an approach that models democracy itself as an information system and explains how democracies are vulnerable to certain forms of information attacks that autocracies naturally resist. Our model combines ideas from both international security and computer security, avoiding the limitations of both in explaining how influence attacks may damage democracy as a whole.

Our initial account is necessarily limited. Building a truly comprehensive understanding of democracy as an information system will be a Herculean labor, involving the collective endeavors of political scientists and theorists, computer scientists, scholars of complexity, and others.

In this short paper, we undertake a more modest task: providing policy advice to improve the resilience of democracy against these attacks. Specifically, we can show how policy makers not only need to think about how to strengthen systems against attacks, but also need to consider how these efforts intersect with public beliefs­ — or common political knowledge­ — about these systems, since public beliefs may themselves be an important vector for attacks.

In democracies, many important political decisions are taken by ordinary citizens (typically, in electoral democracies, by voting for political representatives). This means that citizens need to have some shared understandings about their political system, and that the society needs some means of generating shared information regarding who their citizens are and what they want. We call this common political knowledge, and it is largely generated through mechanisms of social aggregation (and the institutions that implement them), such as voting, censuses, and the like. These are imperfect mechanisms, but essential to the proper functioning of democracy. They are often compromised or non-existent in autocratic regimes, since they are potentially threatening to the rulers.

In modern democracies, the most important such mechanism is voting, which aggregates citizens’ choices over competing parties and politicians to determine who is to control executive power for a limited period. Another important mechanism is the census process, which play an important role in the US and in other democracies, in providing broad information about the population, in shaping the electoral system (through the allocation of seats in the House of Representatives), and in policy making (through the allocation of government spending and resources). Of lesser import are public commenting processes, through which individuals and interest groups can comment on significant public policy and regulatory decisions.

All of these systems are vulnerable to attack. Elections are vulnerable to a variety of illegal manipulations, including vote rigging. However, many kinds of manipulation are currently legal in the US, including many forms of gerrymandering, gimmicking voting time, allocating polling booths and resources so as to advantage or disadvantage particular populations, imposing onerous registration and identity requirements, and so on.

Censuses may be manipulated through the provision of bogus information or, more plausibly, through the skewing of policy or resources so that some populations are undercounted. Many of the political battles over the census over the past few decades have been waged over whether the census should undertake statistical measures to counter undersampling bias for populations who are statistically less likely to return census forms, such as minorities and undocumented immigrants. Current efforts to include a question about immigration status may make it less likely that undocumented or recent immigrants will return completed forms.

Finally, public commenting systems too are vulnerable to attacks intended to misrepresent the support for or opposition to specific proposals, including the formation of astroturf (artificial grassroots) groups and the misuse of fake or stolen identities in large-scale mail, fax, email or online commenting systems.

All these attacks are relatively well understood, even if policy choices might be improved by a better understanding of their relationship to shared political knowledge. For example, some voting ID requirements are rationalized through appeals to security concerns about voter fraud. While political scientists have suggested that these concerns are largely unwarranted, we currently lack a framework for evaluating the trade-offs, if any. Computer security concepts such as confidentiality, integrity, and availability could be combined with findings from political science and political theory to provide such a framework.

Even so, the relationship between social aggregation institutions and public beliefs is far less well understood by policy makers. Even when social aggregation mechanisms and institutions are robust against direct attacks, they may be vulnerable to more indirect attacks aimed at destabilizing public beliefs about them.

Democratic societies are vulnerable to (at least) two kinds of knowledge attacks that autocratic societies are not. First are flooding attacks that create confusion among citizens about what other citizens believe, making it far more difficult for them to organize among themselves. Second are confidence attacks. These attempt to undermine public confidence in the institutions of social aggregation, so that their results are no longer broadly accepted as legitimate representations of the citizenry.

Most obviously, democracies will function poorly when citizens do not believe that voting is fair. This makes democracies vulnerable to attacks aimed at destabilizing public confidence in voting institutions. For example, some of Russia’s hacking efforts against the 2016 presidential election were designed to undermine citizens’ confidence in the result. Russian hacking attacks against Ukraine, which targeted the systems through which election results were reported out, were intended to create confusion among voters about what the outcome actually was. Similarly, the “Guccifer 2.0” hacking identity, which has been attributed to Russian military intelligence, sought to suggest that the US electoral system had been compromised by the Democrats in the days immediately before the presidential vote. If, as expected, Donald Trump had lost the election, these claims could have been combined with the actual evidence of hacking to create the appearance that the election was fundamentally compromised.

Similar attacks against the perception of fairness are likely to be employed against the 2020 US census. Should efforts to include a citizenship question fail, some political actors who are disadvantaged by demographic changes such as increases in foreign-born residents and population shift from rural to urban and suburban areas will mount an effort to delegitimize the census results. Again, the genuine problems with the census, which include not only the citizenship question controversy but also serious underfunding, may help to bolster these efforts.

Mechanisms that allow interested actors and ordinary members of the public to comment on proposed policies are similarly vulnerable. For example, the Federal Communication Commission (FCC) announced in 2017 that it was proposing to repeal its net neutrality ruling. Interest groups backing the FCC rollback correctly anticipated a widespread backlash from a politically active coalition of net neutrality supporters. The result was warfare through public commenting. More than 22 million comments were filed, most of which appeared to be either automatically generated or form letters. Millions of these comments were apparently fake, and attached unsuspecting people’s names and email addresses to comments supporting the FCC’s repeal efforts. The vast majority of comments that were not either form letters or automatically generated opposed the FCC’s proposed ruling. The furor around the commenting process was magnified by claims from inside the FCC (later discredited) that the commenting process had also been subjected to a cyberattack.

We do not yet know the identity and motives of the actors behind the flood of fake comments, although the New York State Attorney-General’s office has issued subpoenas for records from a variety of lobbying and advocacy organizations. However, by demonstrating that the commenting process was readily manipulated, the attack made it less likely that the apparently genuine comments of those opposing the FCC’s proposed ruling would be treated as useful evidence of what the public believed. The furor over purported cyberattacks, and the FCC’s unwillingness itself to investigate the attack, have further undermined confidence in an online commenting system that was intended to make the FCC more open to the US public.

We do not know nearly enough about how democracies function as information systems. Generating a better understanding is itself a major policy challenge, which will require substantial resources and, even more importantly, common understandings and shared efforts across a variety of fields of knowledge that currently don’t really engage with each other.

However, even this basic sketch of democracy’s informational aspects can provide policy makers with some key lessons. The most important is that it may be as important to bolster shared public beliefs about key institutions such as voting, public commenting, and census taking against attack, as to bolster the mechanisms and related institutions themselves.

Specifically, many efforts to mitigate attacks against democratic systems begin with spreading public awareness and alarm about their vulnerabilities. This has the benefit of increasing awareness about real problems, but it may ­ especially if exaggerated for effect ­ damage public confidence in the very social aggregation institutions it means to protect. This may mean, for example, that public awareness efforts about Russian hacking that are based on flawed analytic techniques may themselves damage democracy by exaggerating the consequences of attacks.

More generally, this poses important challenges for policy efforts to secure social aggregation institutions against attacks. How can one best secure the systems themselves without damaging public confidence in them? At a minimum, successful policy measures will not simply identify problems in existing systems, but provide practicable, publicly visible, and readily understandable solutions to mitigate them.

We have focused on the problem of confidence attacks in this short essay, because they are both more poorly understood and more profound than flooding attacks. Given historical experience, democracy can probably survive some amount of disinformation about citizens’ beliefs better than it can survive attacks aimed at its core institutions of aggregation. Policy makers need a better understanding of the relationship between political institutions and social beliefs: specifically, the importance of the social aggregation institutions that allow democracies to understand themselves.

There are some low-hanging fruit. Very often, hardening these institutions against attacks on their confidence will go hand in hand with hardening them against attacks more generally. Thus, for example, reforms to voting that require permanent paper ballots and random auditing would not only better secure voting against manipulation, but would have moderately beneficial consequences for public beliefs too.

There are likely broadly similar solutions for public commenting systems. Here, the informational trade-offs are less profound than for voting, since there is no need to balance the requirement for anonymity (so that no-one can tell who voted for who ex post) against other requirements (to ensure that no-one votes twice or more, no votes are changed and so on). Instead, the balance to be struck is between general ease of access and security, making it easier, for example, to leverage secondary sources to validate identity.

Both the robustness of and public confidence in the US census and the other statistical systems that guide the allocation of resources could be improved by insulating them better from political control. For example, a similar system could be used to appoint the director of the census to that for the US Comptroller-General, requiring bipartisan agreement for appointment, and making it hard to exert post-appointment pressure on the official.

Our arguments also illustrate how some well-intentioned efforts to combat social influence operations may have perverse consequences for general social beliefs. The perception of security is at least as important as the reality of security, and any defenses against information attacks need to address both.

However, we need far better developed intellectual tools if we are to properly understand the trade-offs, instead of proposing clearly beneficial policies, and avoiding straightforward mistakes. Forging such tools will require computer security specialists to start thinking systematically about public beliefs as an integral part of the systems that they seek to defend. It will mean that more military oriented cybersecurity specialists need to think deeply about the functioning of democracy and the capacity of internal as well as external actors to disrupt it, rather than reaching for their standard toolkit of state-level deterrence tools. Finally, specialists in the workings of democracy have to learn how to think about democracy and its trade-offs in specifically informational terms.

This essay was written with Henry Farrell, and has previously appeared on Defusing Disinfo.

Judging Facebook’s Privacy Shift

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/03/judging_faceboo.html

Facebook is making a new and stronger commitment to privacy. Last month, the company hired three of its most vociferous critics and installed them in senior technical positions. And on Wednesday, Mark Zuckerberg wrote that the company will pivot to focus on private conversations over the public sharing that has long defined the platform, even while conceding that “frankly we don’t currently have a strong reputation for building privacy protective services.”

There is ample reason to question Zuckerberg’s pronouncement: The company has made — and broken — many privacy promises over the years. And if you read his 3,000-word post carefully, Zuckerberg says nothing about changing Facebook’s surveillance capitalism business model. All the post discusses is making private chats more central to the company, which seems to be a play for increased market dominance and to counter the Chinese company WeChat.

In security and privacy, the devil is always in the details — and Zuckerberg’s post provides none. But we’ll take him at his word and try to fill in some of the details here. What follows is a list of changes we should expect if Facebook is serious about changing its business model and improving user privacy.

How Facebook treats people on its platform

Increased transparency over advertiser and app accesses to user data. Today, Facebook users can download and view much of the data the company has about them. This is important, but it doesn’t go far enough. The company could be more transparent about what data it shares with advertisers and others and how it allows advertisers to select users they show ads to. Facebook could use its substantial skills in usability testing to help people understand the mechanisms advertisers use to show them ads or the reasoning behind what it chooses to show in user timelines. It could deliver on promises in this area.

Better — and more usable — privacy options. Facebook users have limited control over how their data is shared with other Facebook users and almost no control over how it is shared with Facebook’s advertisers, which are the company’s real customers. Moreover, the controls are buried deep behind complex and confusing menu options. To be fair, some of this is because privacy is complex, and it’s hard to understand the results of different options. But much of this is deliberate; Facebook doesn’t want its users to make their data private from other users.

The company could give people better control over how — and whether — their data is used, shared, and sold. For example, it could allow users to turn off individually targeted news and advertising. By this, we don’t mean simply making those advertisements invisible; we mean turning off the data flows into those tailoring systems. Finally, since most users stick to the default options when it comes to configuring their apps, a changing Facebook could tilt those defaults toward more privacy, requiring less tailoring most of the time.

More user protection from stalking. “Facebook stalking” is often thought of as “stalking light,” or “harmless.” But stalkers are rarely harmless. Facebook should acknowledge this class of misuse and work with experts to build tools that protect all of its users, especially its most vulnerable ones. Such tools should guide normal people away from creepiness and give victims power and flexibility to enlist aid from sources ranging from advocates to police.

Fully ending real-name enforcement. Facebook’s real-names policy, requiring people to use their actual legal names on the platform, hurts people such as activists, victims of intimate partner violence, police officers whose work makes them targets, and anyone with a public persona who wishes to have control over how they identify to the public. There are many ways Facebook can improve on this, from ending enforcement to allowing verifying pseudonyms for everyone­ — not just celebrities like Lady Gaga. Doing so would mark a clear shift.

How Facebook runs its platform

Increased transparency of Facebook’s business practices. One of the hard things about evaluating Facebook is the effort needed to get good information about its business practices. When violations are exposed by the media, as they regularly are, we are all surprised at the different ways Facebook violates user privacy. Most recently, the company used phone numbers provided for two-factor authentication for advertising and networking purposes. Facebook needs to be both explicit and detailed about how and when it shares user data. In fact, a move from discussing “sharing” to discussing “transfers,” “access to raw information,” and “access to derived information” would be a visible improvement.

Increased transparency regarding censorship rules. Facebook makes choices about what content is acceptable on its site. Those choices are controversial, implemented by thousands of low-paid workers quickly implementing unclear rules. These are tremendously hard problems without clear solutions. Even obvious rules like banning hateful words run into challenges when people try to legitimately discuss certain important topics. Whatever Facebook does in this regard, the company needs be more transparent about its processes. It should allow regulators and the public to audit the company’s practices. Moreover, Facebook should share any innovative engineering solutions with the world, much as it currently shares its data center engineering.

Better security for collected user data. There have been numerous examples of attackers targeting cloud service platforms to gain access to user data. Facebook has a large and skilled product security team that says some of the right things. That team needs to be involved in the design trade-offs for features and not just review the near-final designs for flaws. Shutting down a feature based on internal security analysis would be a clear message.

Better data security so Facebook sees less. Facebook eavesdrops on almost every aspect of its users’ lives. On the other hand, WhatsApp — purchased by Facebook in 2014 — provides users with end-to-end encrypted messaging. While Facebook knows who is messaging whom and how often, Facebook has no way of learning the contents of those messages. Recently, Facebook announced plans to combine WhatsApp, Facebook Messenger, and Instagram, extending WhatsApp’s security to the consolidated system. Changing course here would be a dramatic and negative signal.

Collecting less data from outside of Facebook. Facebook doesn’t just collect data about you when you’re on the platform. Because its “like” button is on so many other pages, the company can collect data about you when you’re not on Facebook. It even collects what it calls “shadow profiles” — data about you even if you’re not a Facebook user. This data is combined with other surveillance data the company buys, including health and financial data. Collecting and saving less of this data would be a strong indicator of a new direction for the company.

Better use of Facebook data to prevent violence. There is a trade-off between Facebook seeing less and Facebook doing more to prevent hateful and inflammatory speech. Dozens of people have been killed by mob violence because of fake news spread on WhatsApp. If Facebook were doing a convincing job of controlling fake news without end-to-end encryption, then we would expect to hear how it could use patterns in metadata to handle encrypted fake news.

How Facebook manages for privacy

Create a team measured on privacy and trust. Where companies spend their money tells you what matters to them. Facebook has a large and important growth team, but what team, if any, is responsible for privacy, not as a matter of compliance or pushing the rules, but for engineering? Transparency in how it is staffed relative to other teams would be telling.

Hire a senior executive responsible for trust. Facebook’s current team has been focused on growth and revenue. Its one chief security officer, Alex Stamos, was not replaced when he left in 2018, which may indicate that having an advocate for security on the leadership team led to debate and disagreement. Retaining a voice for security and privacy issues at the executive level, before those issues affected users, was a good thing. Now that responsibility is diffuse. It’s unclear how Facebook measures and assesses its own progress and who might be held accountable for failings. Facebook can begin the process of fixing this by designating a senior executive who is responsible for trust.

Engage with regulators. Much of Facebook’s posturing seems to be an attempt to forestall regulation. Facebook sends lobbyists to Washington and other capitals, and until recently the company sent support staff to politician’s offices. It has secret lobbying campaigns against privacy laws. And Facebook has repeatedly violated a 2011 Federal Trade Commission consent order regarding user privacy. Regulating big technical projects is not easy. Most of the people who understand how these systems work understand them because they build them. Societies will regulate Facebook, and the quality of that regulation requires real education of legislators and their staffs. While businesses often want to avoid regulation, any focus on privacy will require strong government oversight. If Facebook is serious about privacy being a real interest, it will accept both government regulation and community input.

User privacy is traditionally against Facebook’s core business interests. Advertising is its business model, and targeted ads sell better and more profitably — and that requires users to engage with the platform as much as possible. Increased pressure on Facebook to manage propaganda and hate speech could easily lead to more surveillance. But there is pressure in the other direction as well, as users equate privacy with increased control over how they present themselves on the platform.

We don’t expect Facebook to abandon its advertising business model, relent in its push for monopolistic dominance, or fundamentally alter its social networking platforms. But the company can give users important privacy protections and controls without abandoning surveillance capitalism. While some of these changes will reduce profits in the short term, we hope Facebook’s leadership realizes that they are in the best long-term interest of the company.

Facebook talks about community and bringing people together. These are admirable goals, and there’s plenty of value (and profit) in having a sustainable platform for connecting people. But as long as the most important measure of success is short-term profit, doing things that help strengthen communities will fall by the wayside. Surveillance, which allows individually targeted advertising, will be prioritized over user privacy. Outrage, which drives engagement, will be prioritized over feelings of belonging. And corporate secrecy, which allows Facebook to evade both regulators and its users, will be prioritized over societal oversight. If Facebook now truly believes that these latter options are critical to its long-term success as a company, we welcome the changes that are forthcoming.

This essay was co-authored with Adam Shostack, and originally appeared on Medium OneZero. We wrote a similar essay in 2002 about judging Microsoft’s then newfound commitment to security.

Cybersecurity for the Public Interest

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/03/cybersecurity_f_1.html

The Crypto Wars have been waging off-and-on for a quarter-century. On one side is law enforcement, which wants to be able to break encryption, to access devices and communications of terrorists and criminals. On the other are almost every cryptographer and computer security expert, repeatedly explaining that there’s no way to provide this capability without also weakening the security of every user of those devices and communications systems.

It’s an impassioned debate, acrimonious at times, but there are real technologies that can be brought to bear on the problem: key-escrow technologies, code obfuscation technologies, and backdoors with different properties. Pervasive surveillance capitalism — ­as practiced by the Internet companies that are already spying on everyone­ — matters. So does society’s underlying security needs. There is a security benefit to giving access to law enforcement, even though it would inevitably and invariably also give that access to others. However, there is also a security benefit of having these systems protected from all attackers, including law enforcement. These benefits are mutually exclusive. Which is more important, and to what degree?

The problem is that almost no policymakers are discussing this policy issue from a technologically informed perspective, and very few technologists truly understand the policy contours of the debate. The result is both sides consistently talking past each other, and policy proposals — ­that occasionally become law­ — that are technological disasters.

This isn’t sustainable, either for this issue or any of the other policy issues surrounding Internet security. We need policymakers who understand technology, but we also need cybersecurity technologists who understand­ — and are involved in — ­policy. We need public-interest technologists.

Let’s pause at that term. The Ford Foundation defines public-interest technologists as “technology practitioners who focus on social justice, the common good, and/or the public interest.” A group of academics recently wrote that public-interest technologists are people who “study the application of technology expertise to advance the public interest, generate public benefits, or promote the public good.” Tim Berners-Lee has called them “philosophical engineers.” I think of public-interest technologists as people who combine their technological expertise with a public-interest focus: by working on tech policy, by working on a tech project with a public benefit, or by working as a traditional technologist for an organization with a public benefit. Maybe it’s not the best term­ — and I know not everyone likes it­ — but it’s a decent umbrella term that can encompass all these roles.

We need public-interest technologists in policy discussions. We need them on congressional staff, in federal agencies, at non-governmental organizations (NGOs), in academia, inside companies, and as part of the press. In our field, we need them to get involved in not only the Crypto Wars, but everywhere cybersecurity and policy touch each other: the vulnerability equities debate, election security, cryptocurrency policy, Internet of Things safety and security, big data, algorithmic fairness, adversarial machine learning, critical infrastructure, and national security. When you broaden the definition of Internet security, many additional areas fall within the intersection of cybersecurity and policy. Our particular expertise and way of looking at the world is critical for understanding a great many technological issues, such as net neutrality and the regulation of critical infrastructure. I wouldn’t want to formulate public policy about artificial intelligence and robotics without a security technologist involved.

Public-interest technology isn’t new. Many organizations are working in this area, from older organizations like EFF and EPIC to newer ones like Verified Voting and Access Now. Many academic classes and programs combine technology and public policy. My cybersecurity policy class at the Harvard Kennedy School is just one example. Media startups like The Markup are doing technology-driven journalism. There are even programs and initiatives related to public-interest technology inside for-profit corporations.

This might all seem like a lot, but it’s really not. There aren’t enough people doing it, there aren’t enough people who know it needs to be done, and there aren’t enough places to do it. We need to build a world where there is a viable career path for public-interest technologists.

There are many barriers. There’s a report titled A Pivotal Moment that includes this quote: “While we cite individual instances of visionary leadership and successful deployment of technology skill for the public interest, there was a consensus that a stubborn cycle of inadequate supply, misarticulated demand, and an inefficient marketplace stymie progress.”

That quote speaks to the three places for intervention. One: the supply side. There just isn’t enough talent to meet the eventual demand. This is especially acute in cybersecurity, which has a talent problem across the field. Public-interest technologists are a diverse and multidisciplinary group of people. Their backgrounds come from technology, policy, and law. We also need to foster diversity within public-interest technology; the populations using the technology must be represented in the groups that shape the technology. We need a variety of ways for people to engage in this sphere: ways people can do it on the side, for a couple of years between more traditional technology jobs, or as a full-time rewarding career. We need public-interest technology to be part of every core computer-science curriculum, with “clinics” at universities where students can get a taste of public-interest work. We need technology companies to give people sabbaticals to do this work, and then value what they’ve learned and done.

Two: the demand side. This is our biggest problem right now; not enough organizations understand that they need technologists doing public-interest work. We need jobs to be funded across a wide variety of NGOs. We need staff positions throughout the government: executive, legislative, and judiciary branches. President Obama’s US Digital Service should be expanded and replicated; so should Code for America. We need more press organizations that perform this kind of work.

Three: the marketplace. We need job boards, conferences, and skills exchanges­ — places where people on the supply side can learn about the demand.

Major foundations are starting to provide funding in this space: the Ford and MacArthur Foundations in particular, but others as well.

This problem in our field has an interesting parallel with the field of public-interest law. In the 1960s, there was no such thing as public-interest law. The field was deliberately created, funded by organizations like the Ford Foundation. They financed legal aid clinics at universities, so students could learn housing, discrimination, or immigration law. They funded fellowships at organizations like the ACLU and the NAACP. They created a world where public-interest law is valued, where all the partners at major law firms are expected to have done some public-interest work. Today, when the ACLU advertises for a staff attorney, paying one-third to one-tenth normal salary, it gets hundreds of applicants. Today, 20% of Harvard Law School graduates go into public-interest law, and the school has soul-searching seminars because that percentage is so low. Meanwhile, the percentage of computer-science graduates going into public-interest work is basically zero.

This is bigger than computer security. Technology now permeates society in a way it didn’t just a couple of decades ago, and governments move too slowly to take this into account. That means technologists now are relevant to all sorts of areas that they had no traditional connection to: climate change, food safety, future of work, public health, bioengineering.

More generally, technologists need to understand the policy ramifications of their work. There’s a pervasive myth in Silicon Valley that technology is politically neutral. It’s not, and I hope most people reading this today knows that. We built a world where programmers felt they had an inherent right to code the world as they saw fit. We were allowed to do this because, until recently, it didn’t matter. Now, too many issues are being decided in an unregulated capitalist environment where significant social costs are too often not taken into account.

This is where the core issues of society lie. The defining political question of the 20th century was: “What should be governed by the state, and what should be governed by the market?” This defined the difference between East and West, and the difference between political parties within countries. The defining political question of the first half of the 21st century is: “How much of our lives should be governed by technology, and under what terms?” In the last century, economists drove public policy. In this century, it will be technologists.

The future is coming faster than our current set of policy tools can deal with. The only way to fix this is to develop a new set of policy tools with the help of technologists. We need to be in all aspects of public-interest work, from informing policy to creating tools all building the future. The world needs all of our help.

This essay previously appeared in the January/February issue of IEEE Security & Privacy.

Together with the Ford Foundation, I am hosting a one-day mini-track on public-interest technologists at the RSA Conference this week on Thursday. We’ve had some press coverage.

Edited to Add (3/7): More news articles.