Entries Tagged "identification"

Page 5 of 26

Apple's FaceID

This is a good interview with Apple’s SVP of Software Engineering about FaceID.

Honestly, I don’t know what to think. I am confident that Apple is not collecting a photo database, but not optimistic that it can’t be hacked with fake faces. I dislike the fact that the police can point the phone at someone and have it automatically unlock. So this is important:

I also quizzed Federighi about the exact way you “quick disabled” Face ID in tricky scenarios—like being stopped by police, or being asked by a thief to hand over your device.

“On older phones the sequence was to click 5 times [on the power button], but on newer phones like iPhone 8 and iPhone X, if you grip the side buttons on either side and hold them a little while—we’ll take you to the power down [screen]. But that also has the effect of disabling Face ID,” says Federighi. “So, if you were in a case where the thief was asking to hand over your phone—you can just reach into your pocket, squeeze it, and it will disable Face ID. It will do the same thing on iPhone 8 to disable Touch ID.”

That squeeze can be of either volume button plus the power button. This, in my opinion, is an even better solution than the “5 clicks” because it’s less obtrusive. When you do this, it defaults back to your passcode.

More:

It’s worth noting a few additional details here:

  • If you haven’t used Face ID in 48 hours, or if you’ve just rebooted, it will ask for a passcode.
  • If there are 5 failed attempts to Face ID, it will default back to passcode. (Federighi has confirmed that this is what happened in the demo onstage when he was asked for a passcode—it tried to read the people setting the phones up on the podium.)
  • Developers do not have access to raw sensor data from the Face ID array. Instead, they’re given a depth map they can use for applications like the Snap face filters shown onstage. This can also be used in ARKit applications.
  • You’ll also get a passcode request if you haven’t unlocked the phone using a passcode or at all in 6.5 days and if Face ID hasn’t unlocked it in 4 hours.

Also be prepared for your phone to immediately lock every time your sleep/wake button is pressed or it goes to sleep on its own. This is just like Touch ID.

Federighi also noted on our call that Apple would be releasing a security white paper on Face ID closer to the release of the iPhone X. So if you’re a researcher or security wonk looking for more, he says it will have “extreme levels of detail” about the security of the system.

Here’s more about fooling it with fake faces:

Facial recognition has long been notoriously easy to defeat. In 2009, for instance, security researchers showed that they could fool face-based login systems for a variety of laptops with nothing more than a printed photo of the laptop’s owner held in front of its camera. In 2015, Popular Science writer Dan Moren beat an Alibaba facial recognition system just by using a video that included himself blinking.

Hacking FaceID, though, won’t be nearly that simple. The new iPhone uses an infrared system Apple calls TrueDepth to project a grid of 30,000 invisible light dots onto the user’s face. An infrared camera then captures the distortion of that grid as the user rotates his or her head to map the face’s 3-D shape­—a trick similar to the kind now used to capture actors’ faces to morph them into animated and digitally enhanced characters.

It’ll be harder, but I have no doubt that it will be done.

More speculation.

I am not planning on enabling it just yet.

Posted on September 19, 2017 at 6:44 AM • View Comments

Security Flaw in Estonian National ID Card

We have no idea how bad this really is:

On 30 August, an international team of researchers informed the Estonian Information System Authority (RIA) of a vulnerability potentially affecting the digital use of Estonian ID cards. The possible vulnerability affects a total of almost 750,000 ID-cards issued starting from October 2014, including cards issued to e-residents. The ID-cards issued before 16 October 2014 use a different chip and are not affected. Mobile-IDs are also not impacted.

My guess is that it’s worse than the politicians are saying:

According to Peterkop, the current data shows this risk to be theoretical and there is no evidence of anyone’s digital identity being misused. “All ID-card operations are still valid and we will take appropriate actions to secure the functioning of our national digital-ID infrastructure. For example, we have restricted the access to Estonian ID-card public key database to prevent illegal use.”

And because this system is so important in local politics, the effects are significant:

In the light of current events, some Estonian politicians called to postpone the upcoming local elections, due to take place on 16 October. In Estonia, approximately 35% of the voters use digital identity to vote online.

But the Estonian prime minister, Jüri Ratas, said at a press conference on 5 September that “this incident will not affect the course of the Estonian e-state.” Ratas also recommended to use Mobile-IDs where possible. The prime minister said that the State Electoral Office will decide whether it will allow the usage of ID cards at the upcoming local elections.

The Estonian Police and Border Guard estimates it will take approximately two months to fix the issue with faulty cards. The authority will involve as many Estonian experts as possible in the process.

This is exactly the sort of thing I worry about as ID systems become more prevalent and more centralized. Anyone want to place bets on whether a foreign country is going to try to hack the next Estonian election?

Another article.

EDITED TO ADD (9/18): More details.

Posted on September 5, 2017 at 3:23 PM • View Comments

De-Anonymizing Browser History Using Social-Network Data

Interesting research: “De-anonymizing Web Browsing Data with Social Networks“:

Abstract: Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest scale demonstrated de-anonymization to date.

Posted on February 10, 2017 at 8:25 AM • View Comments

Heartbeat as Biometric Password

There’s research in using a heartbeat as a biometric password. No details in the article. My guess is that there isn’t nearly enough entropy in the reproducible biometric, but I might be surprised. The article’s suggestion to use it as a password for health records seems especially problematic. “I’m sorry, but we can’t access the patient’s health records because he’s having a heart attack.”

I wrote about this before here.

Posted on January 19, 2017 at 6:22 AM • View Comments

Firefox Removing Battery Status API

Firefox is removing the battery status API, citing privacy concerns. Here’s the paper that described those concerns:

Abstract. We highlight privacy risks associated with the HTML5 Battery Status API. We put special focus on its implementation in the Firefox browser. Our study shows that websites can discover the capacity of users’ batteries by exploiting the high precision readouts provided by Firefox on Linux. The capacity of the battery, as well as its level, expose a fingerprintable surface that can be used to track web users in short time intervals. Our analysis shows that the risk is much higher for old or used batteries with reduced capacities, as the battery capacity may potentially serve as a tracking identifier. The fingerprintable surface of the API could be drastically reduced without any loss in the API’s functionality by reducing the precision of the readings. We propose minor modifications to Battery Status API and its implementation in the Firefox browser to address the privacy issues presented in the study. Our bug report for Firefox was accepted and a fix is deployed.

W3C is updating the spec. Here’s a battery tracker found in the wild.

Posted on November 7, 2016 at 12:59 PM • View Comments

Using Neural Networks to Identify Blurred Faces

Neural networks are good at identifying faces, even if they’re blurry:

In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects obscured by blurring, pixelation, and a recently-proposed privacy system called P3 can be successfully identified by a neural network trained on image datasets­—in some cases at a more consistent rate than humans.

“We argue that humans may no longer be the ‘gold standard’ for extracting information from visual data,” the researchers write. “Recent advances in machine learning based on artificial neural networks have led to dramatic improvements in the state of the art for automated image recognition. Trained machine learning models now outperform humans on tasks such as object recognition and determining the geographic location of an image.”

Research paper

Posted on September 27, 2016 at 9:39 AM • View Comments

Using Wi-Fi Signals to Identify People by Body Shape

Another paper on using Wi-Fi for surveillance. This one is on identifying people by their body shape. “FreeSense:Indoor Human Identification with WiFi Signals“:

Abstract: Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages WIFI signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding WIFI signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of WIFI. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.

EDITED TO ADD (9/13): Related paper.

Posted on August 30, 2016 at 12:57 PM • View Comments

Anonymization and the Law

Interesting paper: “Anonymization and Risk,” by Ira S. Rubinstein and Woodrow Hartzog:

Abstract: Perfect anonymization of data sets has failed. But the process of protecting data subjects in shared information remains integral to privacy practice and policy. While the deidentification debate has been vigorous and productive, there is no clear direction for policy. As a result, the law has been slow to adapt a holistic approach to protecting data subjects when data sets are released to others. Currently, the law is focused on whether an individual can be identified within a given set. We argue that the better locus of data release policy is on the process of minimizing the risk of reidentification and sensitive attribute disclosure. Process-based data release policy, which resembles the law of data security, will help us move past the limitations of focusing on whether data sets have been “anonymized.” It draws upon different tactics to protect the privacy of data subjects, including accurate deidentification rhetoric, contracts prohibiting reidentification and sensitive attribute disclosure, data enclaves, and query-based strategies to match required protections with the level of risk. By focusing on process, data release policy can better balance privacy and utility where nearly all data exchanges carry some risk.

Posted on July 11, 2016 at 6:31 AM • View Comments

The Fallibility of DNA Evidence

This is a good summary article on the fallibility of DNA evidence. Most interesting to me are the parts on the proprietary algorithms used in DNA matching:

William Thompson points out that Perlin has declined to make public the algorithm that drives the program. “You do have a black-box situation happening here,” Thompson told me. “The data go in, and out comes the solution, and we’re not fully informed of what happened in between.”

Last year, at a murder trial in Pennsylvania where TrueAllele evidence had been introduced, defense attorneys demanded that Perlin turn over the source code for his software, noting that “without it, [the defendant] will be unable to determine if TrueAllele does what Dr. Perlin claims it does.” The judge denied the request.

[…]

When I interviewed Perlin at Cybergenetics headquarters, I raised the matter of transparency. He was visibly annoyed. He noted that he’d published detailed papers on the theory behind TrueAllele, and filed patent applications, too: “We have disclosed not the trade secrets of the source code or the engineering details, but the basic math.”

It’s the same problem as any biometric: we need to know the rates of both false positives and false negatives. And if these algorithms are being used to determine guilt, we have a right to examine them.

EDITED TO ADD (6/13): Three more articles.

Posted on May 31, 2016 at 1:04 PM • View Comments

1 … 3 4 5 6 7 … 26

Sidebar photo of Bruce Schneier by Joe MacInnis.