Tag Archives: academicpapers

The Concept of "Return on Data"

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

This law review article by Noam Kolt, titled “Return on Data,” proposes an interesting new way of thinking of privacy law.

Abstract: Consumers routinely supply personal data to technology companies in exchange for services. Yet, the relationship between the utility (U) consumers gain and the data (D) they supply — “return on data” (ROD) — remains largely unexplored. Expressed as a ratio, ROD = U / D. While lawmakers strongly advocate protecting consumer privacy, they tend to overlook ROD. Are the benefits of the services enjoyed by consumers, such as social networking and predictive search, commensurate with the value of the data extracted from them? How can consumers compare competing data-for-services deals? Currently, the legal frameworks regulating these transactions, including privacy law, aim primarily to protect personal data. They treat data protection as a standalone issue, distinct from the benefits which consumers receive. This article suggests that privacy concerns should not be viewed in isolation, but as part of ROD. Just as companies can quantify return on investment (ROI) to optimize investment decisions, consumers should be able to assess ROD in order to better spend and invest personal data. Making data-for-services transactions more transparent will enable consumers to evaluate the merits of these deals, negotiate their terms and make more informed decisions. Pivoting from the privacy paradigm to ROD will both incentivize data-driven service providers to offer consumers higher ROD, as well as create opportunities for new market entrants.

Cryptanalysis of SIMON-32/64

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/05/cryptanalysis_o_4.html

A weird paper was posted on the Cryptology ePrint Archive (working link is via the Wayback Machine), claiming an attack against the NSA-designed cipher SIMON. You can read some commentary about it here. Basically, the authors claimed an attack so devastating that they would only publish a zero-knowledge proof of their attack. Which they didn’t. Nor did they publish anything else of interest, near as I can tell.

The paper has since been deleted from the ePrint Archive, which feels like the correct decision on someone’s part.

Cryptanalyzing a Pair of Russian Encryption Algorithms

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

A pair of Russia-designed cryptographic algorithms — the Kuznyechik block cipher and the Streebog hash function — have the same flawed S-box that is almost certainly an intentional backdoor. It’s just not the kind of mistake you make by accident, not in 2014.

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.

Stealing Ethereum by Guessing Weak Private Keys

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

Someone is stealing millions of dollars worth of Ethereum by guessing users’ private keys. Normally this should be impossible, but lots of keys seem to be very weak. Researchers are unsure how those weak keys are being generated and used.

Their paper is here.

Vulnerabilities in the WPA3 Wi-Fi Security Protocol

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/04/vulnerabilities_7.html

Researchers have found several vulnerabilities in the WPA3 Wi-Fi security protocol:

The design flaws we discovered can be divided in two categories. The first category consists of downgrade attacks against WPA3-capable devices, and the second category consists of weaknesses in the Dragonfly handshake of WPA3, which in the Wi-Fi standard is better known as the Simultaneous Authentication of Equals (SAE) handshake. The discovered flaws can be abused to recover the password of the Wi-Fi network, launch resource consumption attacks, and force devices into using weaker security groups. All attacks are against home networks (i.e. WPA3-Personal), where one password is shared among all users.

News article. Research paper: “Dragonblood: A Security Analysis of WPA3’s SAE Handshake“:

Abstract: The WPA3 certification aims to secure Wi-Fi networks, and provides several advantages over its predecessor WPA2, such as protection against offline dictionary attacks and forward secrecy. Unfortunately, we show that WPA3 is affected by several design flaws,and analyze these flaws both theoretically and practically. Most prominently, we show that WPA3’s Simultaneous Authentication of Equals (SAE) handshake, commonly known as Dragonfly, is affected by password partitioning attacks. These attacks resemble dictionary attacks and allow an adversary to recover the password by abusing timing or cache-based side-channel leaks. Our side-channel attacks target the protocol’s password encoding method. For instance, our cache-based attack exploits SAE’s hash-to-curve algorithm. The resulting attacks are efficient and low cost: brute-forcing all 8-character lowercase password requires less than 125$in Amazon EC2 instances. In light of ongoing standardization efforts on hash-to-curve, Password-Authenticated Key Exchanges (PAKEs), and Dragonfly as a TLS handshake, our findings are also of more general interest. Finally, we discuss how to mitigate our attacks in a backwards-compatible manner, and explain how minor changes to the protocol could have prevented most of our attack

Maliciously Tampering with Medical Imagery

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

In what I am sure is only a first in many similar demonstrations, researchers are able to add or remove cancer signs from CT scans. The results easily fool radiologists.

I don’t think the medical device industry has thought at all about data integrity and authentication issues. In a world where sensor data of all kinds is undetectably manipulatable, they’re going to have to start.

Research paper. Slashdot thread.

Adversarial Machine Learning against Tesla’s Autopilot

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

Researchers have been able to fool Tesla’s autopilot in a variety of ways, including convincing it to drive into oncoming traffic. It requires the placement of stickers on the road.

Abstract: Keen Security Lab has maintained the security research work on Tesla vehicle and shared our research results on Black Hat USA 2017 and 2018 in a row. Based on the ROOT privilege of the APE (Tesla Autopilot ECU, software version 18.6.1), we did some further interesting research work on this module. We analyzed the CAN messaging functions of APE, and successfully got remote control of the steering system in a contact-less way. We used an improved optimization algorithm to generate adversarial examples of the features (autowipers and lane recognition) which make decisions purely based on camera data, and successfully achieved the adversarial example attack in the physical world. In addition, we also found a potential high-risk design weakness of the lane recognition when the vehicle is in Autosteer mode. The whole article is divided into four parts: first a brief introduction of Autopilot, after that we will introduce how to send control commands from APE to control the steering system when the car is driving. In the last two sections, we will introduce the implementation details of the autowipers and lane recognition features, as well as our adversarial example attacking methods in the physical world. In our research, we believe that we made three creative contributions:

  1. We proved that we can remotely gain the root privilege of APE and control the steering system.
  2. We proved that we can disturb the autowipers function by using adversarial examples in the physical world.
  3. We proved that we can mislead the Tesla car into the reverse lane with minor changes on the road.

You can see the stickers in this photo. They’re unobtrusive.

This is machine learning’s big problem, and I think solving it is a lot harder than many believe.

Recovering Smartphone Typing from Microphone Sounds

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

Yet another side-channel attack on smartphones: “Hearing your touch: A new acoustic side channel on smartphones,” by Ilia Shumailov, Laurent Simon, Jeff Yan, and Ross Anderson.

Abstract: We present the first acoustic side-channel attack that recovers what users type on the virtual keyboard of their touch-screen smartphone or tablet. When a user taps the screen with a finger, the tap generates a sound wave that propagates on the screen surface and in the air. We found the device’s microphone(s) can recover this wave and “hear” the finger’s touch, and the wave’s distortions are characteristic of the tap’s location on the screen. Hence, by recording audio through the built-in microphone(s), a malicious app can infer text as the user enters it on their device. We evaluate the effectiveness of the attack with 45 participants in a real-world environment on an Android tablet and an Android smartphone. For the tablet, we recover 61% of 200 4-digit PIN-codes within 20 attempts, even if the model is not trained with the victim’s data. For the smartphone, we recover 9 words of size 7-13 letters with 50 attempts in a common side-channel attack benchmark. Our results suggest that it not always sufficient to rely on isolation mechanisms such as TrustZone to protect user input. We propose and discuss hardware, operating-system and application-level mechanisms to block this attack more effectively. Mobile devices may need a richer capability model, a more user-friendly notification system for sensor usage and a more thorough evaluation of the information leaked by the underlying hardware.

Blog post.

Friday Squid Blogging: New Research on Squid Camouflage

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/03/friday_squid_bl_669.html

From the New York Times:

Now, a paper published last week in Nature Communications suggests that their chromatophores, previously thought to be mainly pockets of pigment embedded in their skin, are also equipped with tiny reflectors made of proteins. These reflectors aid the squid to produce such a wide array of colors, including iridescent greens and blues, within a second of passing in front of a new background. The research reveals that by using tricks found in other parts of the animal kingdom — like shimmering butterflies and peacocks — squid are able to combine multiple approaches to produce their vivid camouflage.

Researchers studied Doryteuthis pealeii, or the longfin squid.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

I Was Cited in a Court Decision

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

An article I co-wrote — my first law journal article — was cited by the Massachusetts Supreme Judicial Court — the state supreme court — in a case on compelled decryption.

Here’s the first, in footnote 1:

We understand the word “password” to be synonymous with other terms that cell phone users may be familiar with, such as Personal Identification Number or “passcode.” Each term refers to the personalized combination of letters or digits that, when manually entered by the user, “unlocks” a cell phone. For simplicity, we use “password” throughout. See generally, Kerr & Schneier, Encryption Workarounds, 106 Geo. L.J. 989, 990, 994, 998 (2018).

And here’s the second, in footnote 5:

We recognize that ordinary cell phone users are likely unfamiliar with the complexities of encryption technology. For instance, although entering a password “unlocks” a cell phone, the password itself is not the “encryption key” that decrypts the cell phone’s contents. See Kerr & Schneier, supra at 995. Rather, “entering the [password] decrypts the [encryption] key, enabling the key to be processed and unlocking the phone. This two-stage process is invisible to the casual user.” Id. Because the technical details of encryption technology do not play a role in our analysis, they are not worth belaboring. Accordingly, we treat the entry of a password as effectively decrypting the contents of a cell phone. For a more detailed discussion of encryption technology, see generally Kerr & Schneier, supra.

Digital Signatures in PDFs Are Broken

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

Researchers have demonstrated spoofing of digital signatures in PDF files.

This would matter more if PDF digital signatures were widely used. Still, the researchers have worked with the various companies that make PDF readers to close the vulnerabilities. You should update your software.

Details are here.

News article.

Friday Squid Blogging: A Tracking Device for Squid

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/02/friday_squid_bl_664.html

Really:

After years of “making do” with the available technology for his squid studies, Mooney created a versatile tag that allows him to research squid behavior. With the help of Kakani Katija, an engineer adapting the tag for jellyfish at California’s Monterey Bay Aquarium Research Institute (MBARI), Mooney’s team is creating a replicable system flexible enough to work across a range of soft-bodied marine animals. As Mooney and Katija refine the tags, they plan to produce an adaptable, open-source package that scientists researching other marine invertebrates can also use.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Blockchain and Trust

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/02/blockchain_and_.html

In his 2008 white paper that first proposed bitcoin, the anonymous Satoshi Nakamoto concluded with: “We have proposed a system for electronic transactions without relying on trust.” He was referring to blockchain, the system behind bitcoin cryptocurrency. The circumvention of trust is a great promise, but it’s just not true. Yes, bitcoin eliminates certain trusted intermediaries that are inherent in other payment systems like credit cards. But you still have to trust bitcoin — and everything about it.

Much has been written about blockchains and how they displace, reshape, or eliminate trust. But when you analyze both blockchain and trust, you quickly realize that there is much more hype than value. Blockchain solutions are often much worse than what they replace.

First, a caveat. By blockchain, I mean something very specific: the data structures and protocols that make up a public blockchain. These have three essential elements. The first is a distributed (as in multiple copies) but centralized (as in there’s only one) ledger, which is a way of recording what happened and in what order. This ledger is public, meaning that anyone can read it, and immutable, meaning that no one can change what happened in the past.

The second element is the consensus algorithm, which is a way to ensure all the copies of the ledger are the same. This is generally called mining; a critical part of the system is that anyone can participate. It is also distributed, meaning that you don’t have to trust any particular node in the consensus network. It can also be extremely expensive, both in data storage and in the energy required to maintain it. Bitcoin has the most expensive consensus algorithm the world has ever seen, by far.

Finally, the third element is the currency. This is some sort of digital token that has value and is publicly traded. Currency is a necessary element of a blockchain to align the incentives of everyone involved. Transactions involving these tokens are stored on the ledger.

Private blockchains are completely uninteresting. (By this, I mean systems that use the blockchain data structure but don’t have the above three elements.) In general, they have some external limitation on who can interact with the blockchain and its features. These are not anything new; they’re distributed append-only data structures with a list of individuals authorized to add to it. Consensus protocols have been studied in distributed systems for more than 60 years. Append-only data structures have been similarly well covered. They’re blockchains in name only, and — as far as I can tell — the only reason to operate one is to ride on the blockchain hype.

All three elements of a public blockchain fit together as a single network that offers new security properties. The question is: Is it actually good for anything? It’s all a matter of trust.

Trust is essential to society. As a species, humans are wired to trust one another. Society can’t function without trust, and the fact that we mostly don’t even think about it is a measure of how well trust works.

The word “trust” is loaded with many meanings. There’s personal and intimate trust. When we say we trust a friend, we mean that we trust their intentions and know that those intentions will inform their actions. There’s also the less intimate, less personal trust — we might not know someone personally, or know their motivations, but we can trust their future actions. Blockchain enables this sort of trust: We don’t know any bitcoin miners, for example, but we trust that they will follow the mining protocol and make the whole system work.

Most blockchain enthusiasts have a unnaturally narrow definition of trust. They’re fond of catchphrases like “in code we trust,” “in math we trust,” and “in crypto we trust.” This is trust as verification. But verification isn’t the same as trust.

In 2012, I wrote a book about trust and security, Liars and Outliers. In it, I listed four very general systems our species uses to incentivize trustworthy behavior. The first two are morals and reputation. The problem is that they scale only to a certain population size. Primitive systems were good enough for small communities, but larger communities required delegation, and more formalism.

The third is institutions. Institutions have rules and laws that induce people to behave according to the group norm, imposing sanctions on those who do not. In a sense, laws formalize reputation. Finally, the fourth is security systems. These are the wide varieties of security technologies we employ: door locks and tall fences, alarm systems and guards, forensics and audit systems, and so on.

These four elements work together to enable trust. Take banking, for example. Financial institutions, merchants, and individuals are all concerned with their reputations, which prevents theft and fraud. The laws and regulations surrounding every aspect of banking keep everyone in line, including backstops that limit risks in the case of fraud. And there are lots of security systems in place, from anti-counterfeiting technologies to internet-security technologies.

In his 2018 book, Blockchain and the New Architecture of Trust, Kevin Werbach outlines four different “trust architectures.” The first is peer-to-peer trust. This basically corresponds to my morals and reputational systems: pairs of people who come to trust each other. His second is leviathan trust, which corresponds to institutional trust. You can see this working in our system of contracts, which allows parties that don’t trust each other to enter into an agreement because they both trust that a government system will help resolve disputes. His third is intermediary trust. A good example is the credit card system, which allows untrusting buyers and sellers to engage in commerce. His fourth trust architecture is distributed trust. This is emergent trust in the particular security system that is blockchain.

What blockchain does is shift some of the trust in people and institutions to trust in technology. You need to trust the cryptography, the protocols, the software, the computers and the network. And you need to trust them absolutely, because they’re often single points of failure.

When that trust turns out to be misplaced, there is no recourse. If your bitcoin exchange gets hacked, you lose all of your money. If your bitcoin wallet gets hacked, you lose all of your money. If you forget your login credentials, you lose all of your money. If there’s a bug in the code of your smart contract, you lose all of your money. If someone successfully hacks the blockchain security, you lose all of your money. In many ways, trusting technology is harder than trusting people. Would you rather trust a human legal system or the details of some computer code you don’t have the expertise to audit?

Blockchain enthusiasts point to more traditional forms of trust — bank processing fees, for example — as expensive. But blockchain trust is also costly; the cost is just hidden. For bitcoin, that’s the cost of the additional bitcoin mined, the transaction fees, and the enormous environmental waste.

Blockchain doesn’t eliminate the need to trust human institutions. There will always be a big gap that can’t be addressed by technology alone. People still need to be in charge, and there is always a need for governance outside the system. This is obvious in the ongoing debate about changing the bitcoin block size, or in fixing the DAO attack against Ethereum. There’s always a need to override the rules, and there’s always a need for the ability to make permanent rules changes. As long as hard forks are a possibility — that’s when the people in charge of a blockchain step outside the system to change it — people will need to be in charge.

Any blockchain system will have to coexist with other, more conventional systems. Modern banking, for example, is designed to be reversible. Bitcoin is not. That makes it hard to make the two compatible, and the result is often an insecurity. Steve Wozniak was scammed out of $70K in bitcoin because he forgot this.

Blockchain technology is often centralized. Bitcoin might theoretically be based on distributed trust, but in practice, that’s just not true. Just about everyone using bitcoin has to trust one of the few available wallets and use one of the few available exchanges. People have to trust the software and the operating systems and the computers everything is running on. And we’ve seen attacks against wallets and exchanges. We’ve seen Trojans and phishing and password guessing. Criminals have even used flaws in the system that people use to repair their cell phones to steal bitcoin.

Moreover, in any distributed trust system, there are backdoor methods for centralization to creep back in. With bitcoin, there are only a few miners of consequence. There’s one company that provides most of the mining hardware. There are only a few dominant exchanges. To the extent that most people interact with bitcoin, it is through these centralized systems. This also allows for attacks against blockchain-based systems.

These issues are not bugs in current blockchain applications, they’re inherent in how blockchain works. Any evaluation of the security of the system has to take the whole socio-technical system into account. Too many blockchain enthusiasts focus on the technology and ignore the rest.

To the extent that people don’t use bitcoin, it’s because they don’t trust bitcoin. That has nothing to do with the cryptography or the protocols. In fact, a system where you can lose your life savings if you forget your key or download a piece of malware is not particularly trustworthy. No amount of explaining how SHA-256 works to prevent double-spending will fix that.

Similarly, to the extent that people do use blockchains, it is because they trust them. People either own bitcoin or not based on reputation; that’s true even for speculators who own bitcoin simply because they think it will make them rich quickly. People choose a wallet for their cryptocurrency, and an exchange for their transactions, based on reputation. We even evaluate and trust the cryptography that underpins blockchains based on the algorithms’ reputation.

To see how this can fail, look at the various supply-chain security systems that are using blockchain. A blockchain isn’t a necessary feature of any of them. The reasons they’re successful is that everyone has a single software platform to enter their data in. Even though the blockchain systems are built on distributed trust, people don’t necessarily accept that. For example, some companies don’t trust the IBM/Maersk system because it’s not their blockchain.

Irrational? Maybe, but that’s how trust works. It can’t be replaced by algorithms and protocols. It’s much more social than that.

Still, the idea that blockchains can somehow eliminate the need for trust persists. Recently, I received an email from a company that implemented secure messaging using blockchain. It said, in part: “Using the blockchain, as we have done, has eliminated the need for Trust.” This sentiment suggests the writer misunderstands both what blockchain does and how trust works.

Do you need a public blockchain? The answer is almost certainly no. A blockchain probably doesn’t solve the security problems you think it solves. The security problems it solves are probably not the ones you have. (Manipulating audit data is probably not your major security risk.) A false trust in blockchain can itself be a security risk. The inefficiencies, especially in scaling, are probably not worth it. I have looked at many blockchain applications, and all of them could achieve the same security properties without using a blockchain­ — of course, then they wouldn’t have the cool name.

Honestly, cryptocurrencies are useless. They’re only used by speculators looking for quick riches, people who don’t like government-backed currencies, and criminals who want a black-market way to exchange money.

To answer the question of whether the blockchain is needed, ask yourself: Does the blockchain change the system of trust in any meaningful way, or just shift it around? Does it just try to replace trust with verification? Does it strengthen existing trust relationships, or try to go against them? How can trust be abused in the new system, and is this better or worse than the potential abuses in the old system? And lastly: What would your system look like if you didn’t use blockchain at all?

If you ask yourself those questions, it’s likely you’ll choose solutions that don’t use public blockchain. And that’ll be a good thing — especially when the hype dissipates.

This essay previously appeared on Wired.com.

EDITED TO ADD (2/11): Two commentaries on my essay.

I have wanted to write this essay for over a year. The impetus to finally do it came from an invite to speak at the Hyperledger Global Forum in December. This essay is a version of the talk I wrote for that event, made more accessible to a general audience.

It seems to be the season for blockchain takedowns. James Waldo has an excellent essay in Queue. And Nicholas Weaver gave a talk at the Enigma Conference, summarized here. It’s a shortened version of this talk.

EDITED TO ADD (2/17): Reddit thread.

Human Rights by Design

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/12/human_rights_by.html

Good essay: “Advancing Human-Rights-By-Design In The Dual-Use Technology Industry,” by Jonathon Penney, Sarah McKune, Lex Gill, and Ronald J. Deibert:

But businesses can do far more than these basic measures. They could adopt a “human-rights-by-design” principle whereby they commit to designing tools, technologies, and services to respect human rights by default, rather than permit abuse or exploitation as part of their business model. The “privacy-by-design” concept has gained currency today thanks in part to the European Union General Data Protection Regulation (GDPR), which requires it. The overarching principle is that companies must design products and services with the default assumption that they protect privacy, data, and information of data subjects. A similar human-rights-by-design paradigm, for example, would prevent filtering companies from designing their technology with features that enable large-scale, indiscriminate, or inherently disproportionate censorship capabilities­ — like the Netsweeper feature that allows an ISP to block entire country top level domains (TLDs). DPI devices and systems could be configured to protect against the ability of operators to inject spyware in network traffic or redirect users to malicious code rather than facilitate it. And algorithms incorporated into the design of communications and storage platforms could account for human rights considerations in addition to business objectives. Companies could also join multi-stakeholder efforts like the Global Network Initiative (GNI), through which technology companies (including Google, Microsoft, and Yahoo) have taken the first step toward principles like transparency, privacy, and freedom of expression, as well as to self-reporting requirements and independent compliance assessments.

Propaganda and the Weakening of Trust in Government

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/11/propaganda_and_.html

On November 4, 2016, the hacker “Guccifer 2.0,: a front for Russia’s military intelligence service, claimed in a blogpost that the Democrats were likely to use vulnerabilities to hack the presidential elections. On November 9, 2018, President Donald Trump started tweeting about the senatorial elections in Florida and Arizona. Without any evidence whatsoever, he said that Democrats were trying to steal the election through “FRAUD.”

Cybersecurity experts would say that posts like Guccifer 2.0’s are intended to undermine public confidence in voting: a cyber-attack against the US democratic system. Yet Donald Trump’s actions are doing far more damage to democracy. So far, his tweets on the topic have been retweeted over 270,000 times, eroding confidence far more effectively than any foreign influence campaign.

We need new ideas to explain how public statements on the Internet can weaken American democracy. Cybersecurity today is not only about computer systems. It’s also about the ways attackers can use computer systems to manipulate and undermine public expectations about democracy. Not only do we need to rethink attacks against democracy; we also need to rethink the attackers as well.

This is one key reason why we wrote a new research paper which uses ideas from computer security to understand the relationship between democracy and information. These ideas help us understand attacks which destabilize confidence in democratic institutions or debate.

Our research implies that insider attacks from within American politics can be more pernicious than attacks from other countries. They are more sophisticated, employ tools that are harder to defend against, and lead to harsh political tradeoffs. The US can threaten charges or impose sanctions when Russian trolling agencies attack its democratic system. But what punishments can it use when the attacker is the US president?

People who think about cybersecurity build on ideas about confrontations between states during the Cold War. Intellectuals such as Thomas Schelling developed deterrence theory, which explained how the US and USSR could maneuver to limit each other’s options without ever actually going to war. Deterrence theory, and related concepts about the relative ease of attack and defense, seemed to explain the tradeoffs that the US and rival states faced, as they started to use cyber techniques to probe and compromise each others’ information networks.

However, these ideas fail to acknowledge one key differences between the Cold War and today. Nearly all states — whether democratic or authoritarian — are entangled on the Internet. This creates both new tensions and new opportunities. The US assumed that the internet would help spread American liberal values, and that this was a good and uncontroversial thing. Illiberal states like Russia and China feared that Internet freedom was a direct threat to their own systems of rule. Opponents of the regime might use social media and online communication to coordinate among themselves, and appeal to the broader public, perhaps toppling their governments, as happened in Tunisia during the Arab Spring.

This led illiberal states to develop new domestic defenses against open information flows. As scholars like Molly Roberts have shown, states like China and Russia discovered how they could "flood" internet discussion with online nonsense and distraction, making it impossible for their opponents to talk to each other, or even to distinguish between truth and falsehood. These flooding techniques stabilized authoritarian regimes, because they demoralized and confused the regime’s opponents. Libertarians often argue that the best antidote to bad speech is more speech. What Vladimir Putin discovered was that the best antidote to more speech was bad speech.

Russia saw the Arab Spring and efforts to encourage democracy in its neighborhood as direct threats, and began experimenting with counter-offensive techniques. When a Russia-friendly government in Ukraine collapsed due to popular protests, Russia tried to destabilize new, democratic elections by hacking the system through which the election results would be announced. The clear intention was to discredit the election results by announcing fake voting numbers that would throw public discussion into disarray.

This attack on public confidence in election results was thwarted at the last moment. Even so, it provided the model for a new kind of attack. Hackers don’t have to secretly alter people’s votes to affect elections. All they need to do is to damage public confidence that the votes were counted fairly. As researchers have argued, “simply put, the attacker might not care who wins; the losing side believing that the election was stolen from them may be equally, if not more, valuable.”

These two kinds of attacks — “flooding” attacks aimed at destabilizing public discourse, and “confidence” attacks aimed at undermining public belief in elections — were weaponized against the US in 2016. Russian social media trolls, hired by the “Internet Research Agency,” flooded online political discussions with rumors and counter-rumors in order to create confusion and political division. Peter Pomerantsev describes how in Russia, “one moment [Putin’s media wizard] Surkov would fund civic forums and human rights NGOs, the next he would quietly support nationalist movements that accuse the NGOs of being tools of the West.” Similarly, Russian trolls tried to get Black Lives Matter protesters and anti-Black Lives Matter protesters to march at the same time and place, to create conflict and the appearance of chaos. Guccifer 2.0’s blog post was surely intended to undermine confidence in the vote, preparing the ground for a wider destabilization campaign after Hillary Clinton won the election. Neither Putin nor anyone else anticipated that Trump would win, ushering in chaos on a vastly greater scale.

We do not know how successful these attacks were. A new book by John Sides, Michael Tesler and Lynn Vavreck suggests that Russian efforts had no measurable long-term consequences. Detailed research on the flow of news articles through social media by Yochai Benker, Robert Farris, and Hal Roberts agrees, showing that Fox News was far more influential in the spread of false news stories than any Russian effort.

However, global adversaries like the Russians aren’t the only actors who can use flooding and confidence attacks. US actors can use just the same techniques. Indeed, they can arguably use them better, since they have a better understanding of US politics, more resources, and are far more difficult for the government to counter without raising First Amendment issues.

For example, when the Federal Communication Commission asked for comments on its proposal to get rid of “net neutrality,” it was flooded by fake comments supporting the proposal. Nearly every real person who commented was in favor of net neutrality, but their arguments were drowned out by a flood of spurious comments purportedly made by identities stolen from porn sites, by people whose names and email addresses had been harvested without their permission, and, in some cases, from dead people. This was done not just to generate fake support for the FCC’s controversial proposal. It was to devalue public comments in general, making the general public’s support for net neutrality politically irrelevant. FCC decision making on issues like net neutrality used to be dominated by industry insiders, and many would like to go back to the old regime.

Trump’s efforts to undermine confidence in the Florida and Arizona votes work on a much larger scale. There are clear short-term benefits to asserting fraud where no fraud exists. This may sway judges or other public officials to make concessions to the Republicans to preserve their legitimacy. Yet they also destabilize American democracy in the long term. If Republicans are convinced that Democrats win by cheating, they will feel that their own manipulation of the system (by purging voter rolls, making voting more difficult and so on) are legitimate, and very probably cheat even more flagrantly in the future. This will trash collective institutions and leave everyone worse off.

It is notable that some Arizonan Republicans — including Martha McSally — have so far stayed firm against pressure from the White House and the Republican National Committee to claim that cheating is happening. They presumably see more long term value from preserving existing institutions than undermining them. Very plausibly, Donald Trump has exactly the opposite incentives. By weakening public confidence in the vote today, he makes it easier to claim fraud and perhaps plunge American politics into chaos if he is defeated in 2020.

If experts who see Russian flooding and confidence measures as cyberattacks on US democracy are right, then these attacks are just as dangerous — and perhaps more dangerous — when they are used by domestic actors. The risk is that over time they will destabilize American democracy so that it comes closer to Russia’s managed democracy — where nothing is real any more, and ordinary people feel a mixture of paranoia, helplessness and disgust when they think about politics. Paradoxically, Russian interference is far too ineffectual to get us there — but domestically mounted attacks by all-American political actors might.

To protect against that possibility, we need to start thinking more systematically about the relationship between democracy and information. Our paper provides one way to do this, highlighting the vulnerabilities of democracy against certain kinds of information attack. More generally, we need to build levees against flooding while shoring up public confidence in voting and other public information systems that are necessary to democracy.

The first may require radical changes in how we regulate social media companies. Modernization of government commenting platforms to make them robust against flooding is only a very minimal first step. Up until very recently, companies like Twitter have won market advantage from bot infestations — even when it couldn’t make a profit, it seemed that user numbers were growing. CEOs like Mark Zuckerberg have begun to worry about democracy, but their worries will likely only go so far. It is difficult to get a man to understand something when his business model depends on not understanding it. Sharp — and legally enforceable — limits on automated accounts are a first step. Radical redesign of networks and of trending indicators so that flooding attacks are less effective may be a second.

The second requires general standards for voting at the federal level, and a constitutional guarantee of the right to vote. Technical experts nearly universally favor robust voting systems that would combine paper records with random post-election auditing, to prevent fraud and secure public confidence in voting. Other steps to ensure proper ballot design, and standardize vote counting and reporting will take more time and discussion — yet the record of other countries show that they are not impossible.

The US is nearly unique among major democracies in the persistent flaws of its election machinery. Yet voting is not the only important form of democratic information. Apparent efforts to deliberately skew the US census against counting undocumented immigrants show the need for a more general audit of the political information systems that we need if democracy is to function properly.

It’s easier to respond to Russian hackers through sanctions, counter-attacks and the like than to domestic political attacks that undermine US democracy. To preserve the basic political freedoms of democracy requires recognizing that these freedoms are sometimes going to be abused by politicians such as Donald Trump. The best that we can do is to minimize the possibilities of abuse up to the point where they encroach on basic freedoms and harden the general institutions that secure democratic information against attacks intended to undermine them.

This essay was co-authored with Henry Farrell, and previously appeared on Motherboard, with a terrible headline that I was unable to get changed.

Using Machine Learning to Create Fake Fingerprints

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/11/using_machine_l.html

Researchers are able to create fake fingerprints that result in a 20% false-positive rate.

The problem is that these sensors obtain only partial images of users’ fingerprints — at the points where they make contact with the scanner. The paper noted that since partial prints are not as distinctive as complete prints, the chances of one partial print getting matched with another is high.

The artificially generated prints, dubbed DeepMasterPrints by the researchers, capitalize on the aforementioned vulnerability to accurately imitate one in five fingerprints in a database. The database was originally supposed to have only an error rate of one in a thousand.

Another vulnerability exploited by the researchers was the high prevalence of some natural fingerprint features such as loops and whorls, compared to others. With this understanding, the team generated some prints that contain several of these common features. They found that these artificial prints were more likely to match with other prints than would be normally possible.

If this result is robust — and I assume it will be improved upon over the coming years — it will make the current generation of fingerprint readers obsolete as secure biometrics. It also opens a new chapter in the arms race between biometric authentication systems and fake biometrics that can fool them.

More interestingly, I wonder if similar techniques can be brought to bear against other biometrics are well.

Research paper.

Slashdot thread