Tag Archives: identification

Identifying a Person Based on a Photo, LinkedIn and Etsy Profiles, and Other Internet Bread Crumbs

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/06/identifying_a_p.html

Interesting story of how the police can identify someone by following the evidence chain from website to website.

According to filings in Blumenthal’s case, FBI agents had little more to go on when they started their investigation than the news helicopter footage of the woman setting the police car ablaze as it was broadcast live May 30.

It showed the woman, in flame-retardant gloves, grabbing a burning piece of a police barricade that had already been used to set one squad car on fire and tossing it into the police SUV parked nearby. Within seconds, that car was also engulfed in flames.

Investigators discovered other images depicting the same scene on Instagram and the video sharing website Vimeo. Those allowed agents to zoom in and identify a stylized tattoo of a peace sign on the woman’s right forearm.

Scouring other images ­– including a cache of roughly 500 photos of the Philly protest shared by an amateur photographer ­– agents found shots of a woman with the same tattoo that gave a clear depiction of the slogan on her T-shirt.

[…]

That shirt, agents said, was found to have been sold only in one location: a shop on Etsy, the online marketplace for crafters, purveyors of custom-made clothing and jewelry, and other collectibles….

The top review on her page, dated just six days before the protest, was from a user identifying herself as “Xx Mv,” who listed her location as Philadelphia and her username as “alleycatlore.”

A Google search of that handle led agents to an account on Poshmark, the mobile fashion marketplace, with a user handle “lore-elisabeth.” And subsequent searches for that name turned up Blumenthal’s LinkedIn profile, where she identifies herself as a graduate of William Penn Charter School and several yoga and massage therapy training centers.

From there, they located Blumenthal’s Jenkintown massage studio and its website, which featured videos demonstrating her at work. On her forearm, agents discovered, was the same distinctive tattoo that investigators first identified on the arsonist in the original TV video.

The obvious moral isn’t a new one: don’t have a distinctive tattoo. But more interesting is how different pieces of evidence can be strung together in order to identify someone. This particular chain was put together manually, but expect machine learning techniques to be able to do this sort of thing automatically — and for organizations like the NSA to implement them on a broad scale.

Another article did a more detailed analysis, and concludes that the Etsy review was the linchpin.

Note to commenters: political commentary on the protesters or protests will be deleted. There are many other forums on the Internet to discuss that.

Used Tesla Components Contain Personal Information

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/05/used_tesla_comp.html

Used Tesla components, sold on eBay, still contain personal information, even after a factory reset.

This is a decades-old problem. It’s a problem with used hard drives. It’s a problem with used photocopiers and printers. It will be a problem with IoT devices. It’ll be a problem with everything, until we decide that data deletion is a priority.

Me on COVID-19 Contact Tracing Apps

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/05/me_on_covad-19_.html

I was quoted in BuzzFeed:

“My problem with contact tracing apps is that they have absolutely no value,” Bruce Schneier, a privacy expert and fellow at the Berkman Klein Center for Internet & Society at Harvard University, told BuzzFeed News. “I’m not even talking about the privacy concerns, I mean the efficacy. Does anybody think this will do something useful? … This is just something governments want to do for the hell of it. To me, it’s just techies doing techie things because they don’t know what else to do.”

I haven’t blogged about this because I thought it was obvious. But from the tweets and emails I have received, it seems not.

This is a classic identification problem, and efficacy depends on two things: false positives and false negatives.

  • False positives: Any app will have a precise definition of a contact: let’s say it’s less than six feet for more than ten minutes. The false positive rate is the percentage of contacts that don’t result in transmissions. This will be because of several reasons. One, the app’s location and proximity systems — based on GPS and Bluetooth — just aren’t accurate enough to capture every contact. Two, the app won’t be aware of any extenuating circumstances, like walls or partitions. And three, not every contact results in transmission; the disease has some transmission rate that’s less than 100% (and I don’t know what that is).
  • False negatives: This is the rate the app fails to register a contact when an infection occurs. This also will be because of several reasons. One, errors in the app’s location and proximity systems. Two, transmissions that occur from people who don’t have the app (even Singapore didn’t get above a 20% adoption rate for the app). And three, not every transmission is a result of that precisely defined contact — the virus sometimes travels further.

Assume you take the app out grocery shopping with you and it subsequently alerts you of a contact. What should you do? It’s not accurate enough for you to quarantine yourself for two weeks. And without ubiquitous, cheap, fast, and accurate testing, you can’t confirm the app’s diagnosis. So the alert is useless.

Similarly, assume you take the app out grocery shopping and it doesn’t alert you of any contact. Are you in the clear? No, you’re not. You actually have no idea if you’ve been infected.

The end result is an app that doesn’t work. People will post their bad experiences on social media, and people will read those posts and realize that the app is not to be trusted. That loss of trust is even worse than having no app at all.

It has nothing to do with privacy concerns. The idea that contact tracing can be done with an app, and not human health professionals, is just plain dumb.

EDITED TO ADD: This Brookings essay makes much the same point.

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.

Bypassing Apple FaceID’s Liveness Detection Feature

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

Apple’s FaceID has a liveness detection feature, which prevents someone from unlocking a victim’s phone by putting it in front of his face while he’s sleeping. That feature has been hacked:

Researchers on Wednesday during Black Hat USA 2019 demonstrated an attack that allowed them to bypass a victim’s FaceID and log into their phone simply by putting a pair of modified glasses on their face. By merely placing tape carefully over the lenses of a pair glasses and placing them on the victim’s face the researchers demonstrated how they could bypass Apple’s FaceID in a specific scenario. The attack itself is difficult, given the bad actor would need to figure out how to put the glasses on an unconscious victim without waking them up.

Cardiac Biometric

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

MIT Technology Review is reporting about an infrared laser device that can identify people by their unique cardiac signature at a distance:

A new device, developed for the Pentagon after US Special Forces requested it, can identify people without seeing their face: instead it detects their unique cardiac signature with an infrared laser. While it works at 200 meters (219 yards), longer distances could be possible with a better laser. “I don’t want to say you could do it from space,” says Steward Remaly, of the Pentagon’s Combatting Terrorism Technical Support Office, “but longer ranges should be possible.”

Contact infrared sensors are often used to automatically record a patient’s pulse. They work by detecting the changes in reflection of infrared light caused by blood flow. By contrast, the new device, called Jetson, uses a technique known as laser vibrometry to detect the surface movement caused by the heartbeat. This works though typical clothing like a shirt and a jacket (though not thicker clothing such as a winter coat).

[…]

Remaly’s team then developed algorithms capable of extracting a cardiac signature from the laser signals. He claims that Jetson can achieve over 95% accuracy under good conditions, and this might be further improved. In practice, it’s likely that Jetson would be used alongside facial recognition or other identification methods.

Wenyao Xu of the State University of New York at Buffalo has also developed a remote cardiac sensor, although it works only up to 20 meters away and uses radar. He believes the cardiac approach is far more robust than facial recognition. “Compared with face, cardiac biometrics are more stable and can reach more than 98% accuracy,” he says.

I have my usual questions about false positives vs false negatives, how stable the biometric is over time, and whether it works better or worse against particular sub-populations. But interesting nonetheless.

Fingerprinting iPhones

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/05/fingerprinting_7.html

This clever attack allows someone to uniquely identify a phone when you visit a website, based on data from the accelerometer, gyroscope, and magnetometer sensors.

We have developed a new type of fingerprinting attack, the calibration fingerprinting attack. Our attack uses data gathered from the accelerometer, gyroscope and magnetometer sensors found in smartphones to construct a globally unique fingerprint. Overall, our attack has the following advantages:

  • The attack can be launched by any website you visit or any app you use on a vulnerable device without requiring any explicit confirmation or consent from you.
  • The attack takes less than one second to generate a fingerprint.
  • The attack can generate a globally unique fingerprint for iOS devices.
  • The calibration fingerprint never changes, even after a factory reset.
  • The attack provides an effective means to track you as you browse across the web and move between apps on your phone.

* Following our disclosure, Apple has patched this vulnerability in iOS 12.2.

Research paper.

Using a Fake Hand to Defeat Hand-Vein Biometrics

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/01/using_a_fake_ha.html

Nice work:

One attraction of a vein based system over, say, a more traditional fingerprint system is that it may be typically harder for an attacker to learn how a user’s veins are positioned under their skin, rather than lifting a fingerprint from a held object or high quality photograph, for example.

But with that said, Krissler and Albrecht first took photos of their vein patterns. They used a converted SLR camera with the infrared filter removed; this allowed them to see the pattern of the veins under the skin.

“It’s enough to take photos from a distance of five meters, and it might work to go to a press conference and take photos of them,” Krissler explained. In all, the pair took over 2,500 pictures to over 30 days to perfect the process and find an image that worked.

They then used that image to make a wax model of their hands which included the vein detail.

Slashdot thread.

MD5 and SHA-1 Still Used in 2018

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/12/md5_and_sha-1_s.html

Last week, the Scientific Working Group on Digital Evidence published a draft document — “SWGDE Position on the Use of MD5 and SHA1 Hash Algorithms in Digital and Multimedia Forensics” — where it accepts the use of MD5 and SHA-1 in digital forensics applications:

While SWGDE promotes the adoption of SHA2 and SHA3 by vendors and practitioners, the MD5 and SHA1 algorithms remain acceptable for integrity verification and file identification applications in digital forensics. Because of known limitations of the MD5 and SHA1 algorithms, only SHA2 and SHA3 are appropriate for digital signatures and other security applications.

This is technically correct: the current state of cryptanalysis against MD5 and SHA-1 allows for collisions, but not for pre-images. Still, it’s really bad form to accept these algorithms for any purpose. I’m sure the group is dealing with legacy applications, but I would like it to really push those application vendors to update their hash functions.

Kidnapping Fraud

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/kidnapping_frau.html

Fake kidnapping fraud:

“Most commonly we have unsolicited calls to potential victims in Australia, purporting to represent the people in authority in China and suggesting to intending victims here they have been involved in some sort of offence in China or elsewhere, for which they’re being held responsible,” Commander McLean said.

The scammers threaten the students with deportation from Australia or some kind of criminal punishment.

The victims are then coerced into providing their identification details or money to get out of the supposed trouble they’re in.

Commander McLean said there are also cases where the student is told they have to hide in a hotel room, provide compromising photos of themselves and cut off all contact.

This simulates a kidnapping.

“So having tricked the victims in Australia into providing the photographs, and money and documents and other things, they then present the information back to the unknowing families in China to suggest that their children who are abroad are in trouble,” Commander McLean said.

“So quite circular in a sense…very skilled, very cunning.”

Detecting Lies through Mouse Movements

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/detecting_lies_.html

Interesting research: “The detection of faked identity using unexpected questions and mouse dynamics,” by Merulin Monaro, Luciano Gamberini, and Guiseppe Sartori.

Abstract: The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent’s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to “build” and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee.

Boing Boing post.

AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-1-click-use-simple-devices-to-trigger-lambda-functions/

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera ControlTim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

Jeff;

 

NIST Issues Call for "Lightweight Cryptography" Algorithms

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/nist_issues_cal.html

This is interesting:

Creating these defenses is the goal of NIST’s lightweight cryptography initiative, which aims to develop cryptographic algorithm standards that can work within the confines of a simple electronic device. Many of the sensors, actuators and other micromachines that will function as eyes, ears and hands in IoT networks will work on scant electrical power and use circuitry far more limited than the chips found in even the simplest cell phone. Similar small electronics exist in the keyless entry fobs to newer-model cars and the Radio Frequency Identification (RFID) tags used to locate boxes in vast warehouses.

All of these gadgets are inexpensive to make and will fit nearly anywhere, but common encryption methods may demand more electronic resources than they possess.

The NSA’s SIMON and SPECK would certainly qualify.

Lifting a Fingerprint from a Photo

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/lifting_a_finge.html

Police in the UK were able to read a fingerprint from a photo of a hand:

Staff from the unit’s specialist imaging team were able to enhance a picture of a hand holding a number of tablets, which was taken from a mobile phone, before fingerprint experts were able to positively identify that the hand was that of Elliott Morris.

[…]

Speaking about the pioneering techniques used in the case, Dave Thomas, forensic operations manager at the Scientific Support Unit, added: “Specialist staff within the JSIU fully utilised their expert image-enhancing skills which enabled them to provide something that the unit’s fingerprint identification experts could work. Despite being provided with only a very small section of the fingerprint which was visible in the photograph, the team were able to successfully identify the individual.”

COPPA Compliance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/coppa_complianc.html

Interesting research: “‘Won’t Somebody Think of the Children?’ Examining COPPA Compliance at Scale“:

Abstract: We present a scalable dynamic analysis framework that allows for the automatic evaluation of the privacy behaviors of Android apps. We use our system to analyze mobile apps’ compliance with the Children’s Online Privacy Protection Act (COPPA), one of the few stringent privacy laws in the U.S. Based on our automated analysis of 5,855 of the most popular free children’s apps, we found that a majority are potentially in violation of COPPA, mainly due to their use of third-party SDKs. While many of these SDKs offer configuration options to respect COPPA by disabling tracking and behavioral advertising, our data suggest that a majority of apps either do not make use of these options or incorrectly propagate them across mediation SDKs. Worse, we observed that 19% of children’s apps collect identifiers or other personally identifiable information (PII) via SDKs whose terms of service outright prohibit their use in child-directed apps. Finally, we show that efforts by Google to limit tracking through the use of a resettable advertising ID have had little success: of the 3,454 apps that share the resettable ID with advertisers, 66% transmit other, non-resettable, persistent identifiers as well, negating any intended privacy-preserving properties of the advertising ID.