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COPPA Compliance

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.

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Tracing Stolen Bitcoin

Ross Anderson has a really interesting paper on tracing stolen bitcoin. From a blog post:

Previous attempts to track tainted coins had used either the “poison” or the “haircut” method. Suppose I open a new address and pay into it three stolen bitcoin followed by seven freshly-mined ones. Then under poison, the output is ten stolen bitcoin, while under haircut it’s ten bitcoin that are marked 30% stolen. After thousands of blocks, poison tainting will blacklist millions of addresses, while with haircut the taint gets diffused, so neither is very effective at tracking stolen property. Bitcoin due-diligence services supplant haircut taint tracking with AI/ML, but the results are still not satisfactory.

We discovered that, back in 1816, the High Court had to tackle this problem in Clayton’s case, which involved the assets and liabilities of a bank that had gone bust. The court ruled that money must be tracked through accounts on the basis of first-in, first out (FIFO); the first penny into an account goes to satisfy the first withdrawal, and so on.

Ilia Shumailov has written software that applies FIFO tainting to the blockchain and the results are impressive, with a massive improvement in precision. What’s more, FIFO taint tracking is lossless, unlike haircut; so in addition to tracking a stolen coin forward to find where it’s gone, you can start with any UTXO and trace it backwards to see its entire ancestry. It’s not just good law; it’s good computer science too.

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Tracking People Without GPS

Interesting research:

The trick in accurately tracking a person with this method is finding out what kind of activity they’re performing. Whether they’re walking, driving a car, or riding in a train or airplane, it’s pretty easy to figure out when you know what you’re looking for.

The sensors can determine how fast a person is traveling and what kind of movements they make. Moving at a slow pace in one direction indicates walking. Going a little bit quicker but turning at 90-degree angles means driving. Faster yet, we’re in train or airplane territory. Those are easy to figure out based on speed and air pressure.

After the app determines what you’re doing, it uses the information it collects from the sensors. The accelerometer relays your speed, the magnetometer tells your relation to true north, and the barometer offers up the air pressure around you and compares it to publicly available information. It checks in with The Weather Channel to compare air pressure data from the barometer to determine how far above sea level you are. Google Maps and data offered by the US Geological Survey Maps provide incredibly detailed elevation readings.

Once it has gathered all of this information and determined the mode of transportation you’re currently taking, it can then begin to narrow down where you are. For flights, four algorithms begin to estimate the target’s location and narrows down the possibilities until its error rate hits zero.

If you’re driving, it can be even easier. The app knows the time zone you’re in based on the information your phone has provided to it. It then accesses information from your barometer and magnetometer and compares it to information from publicly available maps and weather reports. After that, it keeps track of the turns you make. With each turn, the possible locations whittle down until it pinpoints exactly where you are.

To demonstrate how accurate it is, researchers did a test run in Philadelphia. It only took 12 turns before the app knew exactly where the car was.

This is a good example of how powerful synthesizing information from disparate data sources can be. We spend too much time worried about individual data collection systems, and not enough about analysis techniques of those systems.

Research paper.

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E-Mail Tracking

Interesting survey paper: on the privacy implications of e-mail tracking:

Abstract: We show that the simple act of viewing emails contains privacy pitfalls for the unwary. We assembled a corpus of commercial mailing-list emails, and find a network of hundreds of third parties that track email recipients via methods such as embedded pixels. About 30% of emails leak the recipient’s email address to one or more of these third parties when they are viewed. In the majority of cases, these leaks are intentional on the part of email senders, and further leaks occur if the recipient clicks links in emails. Mail servers and clients may employ a variety of defenses, but we analyze 16 servers and clients and find that they are far from comprehensive. We propose, prototype, and evaluate a new defense, namely stripping tracking tags from emails based on enhanced versions of existing web tracking protection lists.

Blog post on the research.

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Using Ultrasonic Beacons to Track Users

I’ve previously written about ad networks using ultrasonic communications to jump from one device to another. The idea is for devices like televisions to play ultrasonic codes in advertisements and for nearby smartphones to detect them. This way the two devices can be linked.

Creepy, yes. And also increasingly common, as this research demonstrates:

Privacy Threats through Ultrasonic Side Channels on Mobile Devices

by Daniel Arp, Erwin Quiring, Christian Wressnegger and Konrad Rieck

Abstract: Device tracking is a serious threat to the privacy of users, as it enables spying on their habits and activities. A recent practice embeds ultrasonic beacons in audio and tracks them using the microphone of mobile devices. This side channel allows an adversary to identify a user’s current location, spy on her TV viewing habits or link together her different mobile devices. In this paper, we explore the capabilities, the current prevalence and technical limitations of this new tracking technique based on three commercial tracking solutions. To this end, we develop detection approaches for ultrasonic beacons and Android applications capable of processing these. Our findings confirm our privacy concerns: We spot ultrasonic beacons in various web media content and detect signals in 4 of 35 stores in two European cities that are used for location tracking. While we do not find ultrasonic beacons in TV streams from 7 countries, we spot 234 Android applications that are constantly listening for ultrasonic beacons in the background without the user’s knowledge.

News article. BoingBoing post.

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Reading Analytics and Privacy

Interesting paper: “The rise of reading analytics and the emerging calculus of reading privacy in the digital world,” by Clifford Lynch:

Abstract: This paper studies emerging technologies for tracking reading behaviors (“reading analytics”) and their implications for reader privacy, attempting to place them in a historical context. It discusses what data is being collected, to whom it is available, and how it might be used by various interested parties (including authors). I explore means of tracking what’s being read, who is doing the reading, and how readers discover what they read. The paper includes two case studies: mass-market e-books (both directly acquired by readers and mediated by libraries) and scholarly journals (usually mediated by academic libraries); in the latter case I also provide examples of the implications of various authentication, authorization and access management practices on reader privacy. While legal issues are touched upon, the focus is generally pragmatic, emphasizing technology and marketplace practices. The article illustrates the way reader privacy concerns are shifting from government to commercial surveillance, and the interactions between government and the private sector in this area. The paper emphasizes U.S.-based developments.

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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.

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