Entries Tagged "identification"

Page 7 of 27

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

Identifying People from their Driving Patterns

People can be identified from their “driver fingerprint“:

…a group of researchers from the University of Washington and the University of California at San Diego found that they could “fingerprint” drivers based only on data they collected from internal computer network of the vehicle their test subjects were driving, what’s known as a car’s CAN bus. In fact, they found that the data collected from a car’s brake pedal alone could let them correctly distinguish the correct driver out of 15 individuals about nine times out of ten, after just 15 minutes of driving. With 90 minutes driving data or monitoring more car components, they could pick out the correct driver fully 100 percent of the time.

The paper: “Automobile Driver Fingerprinting,” by Miro Enev, Alex Takahuwa, Karl Koscher, and Tadayoshi Kohno.

Abstract: Today’s automobiles leverage powerful sensors and embedded computers to optimize efficiency, safety, and driver engagement. However the complexity of possible inferences using in-car sensor data is not well understood. While we do not know of attempts by automotive manufacturers or makers of after-market components (like insurance dongles) to violate privacy, a key question we ask is: could they (or their collection and later accidental leaks of data) violate a driver’s privacy? In the present study, we experimentally investigate the potential to identify individuals using sensor data snippets of their natural driving behavior. More specifically we record the in-vehicle sensor data on the controller area-network (CAN) of a typical modern vehicle (popular 2009 sedan) as each of 15 participants (a) performed a series of maneuvers in an isolated parking lot, and (b) drove the vehicle in traffic along a defined ~50 mile loop through the Seattle metropolitan area. We then split the data into training and testing sets, train an ensemble of classifiers, and evaluate identification accuracy of test data queries by looking at the highest voted candidate when considering all possible one-vs-one comparisons. Our results indicate that, at least among small sets, drivers are indeed distinguishable using only in car sensors. In particular, we find that it is possible to differentiate our 15 drivers with 100% accuracy when training with all of the available sensors using 90% of driving data from each person. Furthermore, it is possible to reach high identification rates using less than 8 minutes of training data. When more training data is available it is possible to reach very high identification using only a single sensor (e.g., the brake pedal). As an extension, we also demonstrate the feasibility of performing driver identification across multiple days of data collection.

Posted on May 30, 2016 at 10:10 AM • View Comments

Identifying People from Their Metadata

Jonathan Mayer, Patrick Mutchler, and John C. Mitchell, “Evaluating the privacy properties of telephone metadata“:

Abstract: Since 2013, a stream of disclosures has prompted reconsideration of surveillance law and policy. One of the most controversial principles, both in the United States and abroad, is that communications metadata receives substantially less protection than communications content. Several nations currently collect telephone metadata in bulk, including on their own citizens. In this paper, we attempt to shed light on the privacy properties of telephone metadata. Using a crowdsourcing methodology, we demonstrate that telephone metadata is densely interconnected, can trivially be reidentified, and can be used to draw sensitive inferences.

New research, but not a new result. There have been several similar studies over the years. This one uses only anonymized call and SMS metadata to identify people who volunteered for the study.

Four assorted news articles.

Posted on May 19, 2016 at 6:10 AM • View Comments

Determining Physical Location on the Internet

Interesting research: “CPV: Delay-based Location Verification for the Internet“:

Abstract: The number of location-aware services over the Internet continues growing. Some of these require the client’s geographic location for security-sensitive applications. Examples include location-aware authentication, location-aware access policies, fraud prevention, complying with media licensing, and regulating online gambling/voting. An adversary can evade existing geolocation techniques, e.g., by faking GPS coordinates or employing a non-local IP address through proxy and virtual private networks. We devise Client Presence Verification (CPV), a delay-based verification technique designed to verify an assertion about a device’s presence inside a prescribed geographic region. CPV does not identify devices by their IP addresses. Rather, the device’s location is corroborated in a novel way by leveraging geometric properties of triangles, which prevents an adversary from manipulating measured delays. To achieve high accuracy, CPV mitigates Internet path asymmetry using a novel method to deduce one-way application-layer delays to/from the client’s participating device, and mines these delays for evidence supporting/refuting the asserted location. We evaluate CPV through detailed experiments on PlanetLab, exploring various factors that affect its efficacy, including the granularity of the verified location, and the verification time. Results highlight the potential of CPV for practical adoption.

News articles.

Posted on February 12, 2016 at 6:19 AM • View Comments

Stealing Fingerprints

The news from the Office of Personnel Management hack keeps getting worse. In addition to the personal records of over 20 million US government employees, we’ve now learned that the hackers stole fingerprint files for 5.6 million of them.

This is fundamentally different from the data thefts we regularly read about in the news, and should give us pause before we entrust our biometric data to large networked databases.

There are three basic kinds of data that can be stolen. The first, and most common, is authentication credentials. These are passwords and other information that allows someone else access into our accounts and—usually—our money. An example would be the 56 million credit card numbers hackers stole from Home Depot in 2014, or the 21.5 million Social Security numbers hackers stole in the OPM breach. The motivation is typically financial. The hackers want to steal money from our bank accounts, process fraudulent credit card charges in our name, or open new lines of credit or apply for tax refunds.

It’s a huge illegal business, but we know how to deal with it when it happens. We detect these hacks as quickly as possible, and update our account credentials as soon as we detect an attack. (We also need to stop treating Social Security numbers as if they were secret.)

The second kind of data stolen is personal information. Examples would be the medical data stolen and exposed when Sony was hacked in 2014, or the very personal data from the infidelity website Ashley Madison stolen and published this year. In these instances, there is no real way to recover after a breach. Once the data is public, or in the hands of an adversary, it’s impossible to make it private again.

This is the main consequence of the OPM data breach. Whoever stole the data—we suspect it was the Chinese—got copies the security-clearance paperwork of all those government employees. This documentation includes the answers to some very personal and embarrassing questions, and now opens these employees up to blackmail and other types of coercion.

Fingerprints are another type of data entirely. They’re used to identify people at crime scenes, but increasingly they’re used as an authentication credential. If you have an iPhone, for example, you probably use your fingerprint to unlock your phone. This type of authentication is increasingly common, replacing a password—something you know—with a biometric: something you are. The problem with biometrics is that they can’t be replaced. So while it’s easy to update your password or get a new credit card number, you can’t get a new finger.

And now, for the rest of their lives, 5.6 million US government employees need to remember that someone, somewhere, has their fingerprints. And we really don’t know the future value of this data. If, in twenty years, we routinely use our fingerprints at ATM machines, that fingerprint database will become very profitable to criminals. If fingerprints start being used on our computers to authorize our access to files and data, that database will become very profitable to spies.

Of course, it’s not that simple. Fingerprint readers employ various technologies to prevent being fooled by fake fingers: detecting temperature, pores, a heartbeat, and so on. But this is an arms race between attackers and defenders, and there are many ways to fool fingerprint readers. When Apple introduced its iPhone fingerprint reader, hackers figured out how to fool it within days, and have continued to fool each new generation of phone readers equally quickly.

Not every use of biometrics requires the biometric data to be stored in a central server somewhere. Apple’s system, for example, only stores the data locally: on your phone. That way there’s no central repository to be hacked. And many systems don’t store the biometric data at all, only a mathematical function of the data that can be used for authentication but can’t be used to reconstruct the actual biometric. Unfortunately, OPM stored copies of actual fingerprints.

Ashley Madison has taught us all the dangers of entrusting our intimate secrets to a company’s computers and networks, because once that data is out there’s no getting it back. All biometric data, whether it be fingerprints, retinal scans, voiceprints, or something else, has that same property. We should be skeptical of any attempts to store this data en masse, whether by governments or by corporations. We need our biometrics for authentication, and we can’t afford to lose them to hackers.

This essay previously appeared on Motherboard.

Posted on October 2, 2015 at 6:35 AM • View Comments

Yet Another New Biometric: Brainprints

New research:

In “Brainprint,” a newly published study in academic journal Neurocomputing, researchers from Binghamton University observed the brain signals of 45 volunteers as they read a list of 75 acronyms, such as FBI and DVD. They recorded the brain’s reaction to each group of letters, focusing on the part of the brain associated with reading and recognizing words, and found that participants’ brains reacted differently to each acronym, enough that a computer system was able to identify each volunteer with 94 percent accuracy. The results suggest that brainwaves could be used by security systems to verify a person’s identity.

I have no idea what the false negatives are, or how robust this biometric is over time, but the article makes the important point that unlike most biometrics this one can be updated.

“If someone’s fingerprint is stolen, that person can’t just grow a new finger to replace the compromised fingerprint—the fingerprint for that person is compromised forever. Fingerprints are ‘non-cancellable.’ Brainprints, on the other hand, are potentially cancellable. So, in the unlikely event that attackers were actually able to steal a brainprint from an authorized user, the authorized user could then ‘reset’ their brainprint,” Laszlo said.

Presumably the resetting involves a new set of acronyms.

Author’s self-archived version of the paper (pdf).

Posted on June 4, 2015 at 10:36 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.