February 28, 2015

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We can de-anonymize programmers from coding style. What are the implications?

In a recent post, I talked about our paper showing how to identify anonymous programmers from their coding styles. We used a combination of lexical features (e.g., variable name choices), layout features (e.g., spacing), and syntactic features (i.e., grammatical structure of source code) to represent programmers’ coding styles. The previous post focused on the overall results and techniques we used. Today I’ll talk about applications and explain how source code authorship attribution can be used in software forensics, plagiarism detection, copyright or copyleft investigations, and other domains.

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Android WebView security and the mobile advertising marketplace

Freedom to Tinker readers are probably aware of the current controversy over Google’s handling of ongoing security vulnerabilities in its Android WebView component. What sounds at first like a routine security problem turns out to have some deep challenges.  Let’s start by filling in some background and build up to the big problem they’re not talking about: Android advertising.
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Anonymous programmers can be identified by analyzing coding style

Every programmer learns to code in a unique way which results in distinguishing “fingerprints” in coding style. These fingerprints can be used to compare the source code of known programmers with an anonymous piece of source code to find out which one of the known programmers authored the anonymous code. This method can aid in finding malware programmers or detecting cases of plagiarism. In a recent paper, we studied this question, which we call source-code authorship attribution. We introduced a principled method with a robust feature set and achieved a breakthrough in accuracy.

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Verizon’s tracking header: Can they do better?

Verizon’s practice of injecting a unique ID into the HTTP headers of traffic originating on their wireless network has alarmed privacy advocates and researchers. Jonathan Mayer detailed how this header is already being used by third-parties to create zombie cookies. In this post, I summarize just how much information Verizon collects and shares under their marketing programs. I’ll show how the implementation of the header makes previous tracking methods trivial and explore the possibility of a more secure design.

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How cookies can be used for global surveillance

Today we present an updated version of our paper examining how the ubiquitous use of online tracking cookies can allow an adversary conducting network surveillance to target a user or surveil users en masse. In the initial version of the study, summarized below, we examined the technical feasibility of the attack. Now we’ve made the attack model more complete and nuanced as well as analyzed the effectiveness of several browser privacy tools in preventing the attack. Finally, inspired by Jonathan Mayer and Ed Felten’s The Web is Flat study, we incorporate the geographic topology of the Internet into our measurements of simulated web traffic and our adversary model, providing a more realistic view of how effective this attack is in practice. [Read more...]

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Striking a balance between advertising and ad blocking

In the news, we have a consortium of French publishers, which somehow includes several major U.S. corporations (Google, Microsoft), attempting to sue AdBlock Plus developer Eyeo, a German firm with developers around the world. I have no idea of the legal basis for their case, but it’s all about the money. AdBlock Plus and the closely related AdBlock are among the most popular Chrome extensions, by far, and publishers will no doubt claim huge monetary damages around presumed “lost income”.
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How do we decide how much to reveal? (Hint: Our privacy behavior might be socially constructed.)

[Let's welcome Aylin Caliskan-Islam, a graduate student at Drexel. In this post she discusses new work that applies machine learning and natural-language processing to questions of privacy and social behavior. — Arvind Narayanan.]

How do we decide how much to share online given that information can spread to millions in large social networks? Is it always our own decision or are we influenced by our friends? Let’s isolate this problem to one variable, private information. How much private information are we sharing in our posts and are we the only authority controlling how much private information to divulge in our textual messages? Understanding how privacy behavior is formed could give us key insights for choosing our privacy settings, friends circles, and how much privacy to sacrifice in social networks. Christakis and Fowler’s network analytics study showed that obesity spreads through social ties. In another study, they explain that smoking cessation is a collective behavior. Our intuition before analyzing end users’ privacy behavior was that privacy behavior might also be under the effect of network phenomena.

In a recent paper that appeared at the 2014 Workshop on Privacy in the Electronic Society, we present a novel method for quantifying privacy behavior of users by using machine learning classifiers and natural-language processing techniques including topic categorization, named entity recognition, and semantic classification. Following the intuition that some textual data is more private than others, we had Amazon Mechanical Turk workers label tweets of hundreds of users as private or not based on nine privacy categories that were influenced by Wang et al.’s Facebook regrets categories and Sleeper et al.’s Twitter regrets categories. These labels were used to associate a privacy score with each user to reflect the amount of private information they reveal. We trained a machine learning classifier based on the calculated privacy scores to predict the privacy scores of 2,000 Twitter users whose data were collected through the Twitter API.
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The hidden perils of cookie syncing

[Steven Englehardt is a first-year Ph.D. student in the computer security group at Princeton. In this post he talks about the implications of a recent study that we published in collaboration with researchers at KU Leuven, Belgium. — Arvind Narayanan]

Online tracking is becoming more sophisticated and thus increasingly difficult to block. Modern browsers expose many surfaces that enable users to be uniquely identified, including Flash cookies and browser fingerprints. In a new paper that will appear at ACM CCS, we present the first large scale study of three advanced tracking mechanisms — canvas fingerprinting, evercookies, and cookie syncing. We developed novel measurement techniques and found that these tracking mechanisms are used on a large number of sites. Our findings on canvas fingerprinting, in particular, have been in the news (Propublica, BBC, EFF).

In this blog post I’ll focus on a different part of our paper that looked at cookie syncing, the process by which two different trackers link the IDs they’ve given to the same user. The most common use of cookie syncing is to enable real-time bidding between several entities in an ad auction. It allows the bidder and the ad network to refer to the user by the same ID so that the bidder can place bids on a particular user in current and future auctions. Cookie syncing raises subtle yet serious privacy concerns, but due to the technical complexity of explaining it, didn’t receive much press coverage. In this post I’ll explain cookie syncing and why it’s worrisome — even more so than canvas fingerprinting.
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A Scanner Darkly: Protecting User Privacy from Perceptual Applications

“A Scanner Darkly”, a dystopian 1977 Philip K. Dick novel (adapted to a 2006 film), describes a society with pervasive audio and video surveillance. Our paper “A Scanner Darkly”, which appeared in last year’s IEEE Symposium on Security and Privacy (Oakland) and has just received the 2014 PET Award for Outstanding Research in Privacy Enhancing Technologies, takes a closer look at the soon-to-come world where ubiquitous surveillance is performed not by the drug police but by everyday devices with high-bandwidth sensors. [Read more...]

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“Loopholes for Circumventing the Constitution”, the NSA Statement, and Our Response

CBS News and a host of other outlets have covered my new paper with Sharon Goldberg, Loopholes for Circumventing the Constitution: Warrantless Bulk Surveillance on Americans by Collecting Network Traffic Abroad. We’ll present the paper on July 18 at HotPETS [slides, pdf], right after a keynote by Bill Binney (the NSA whistleblower), and at TPRC in September. Meanwhile, the NSA has responded to our paper in a clever way that avoids addressing what our paper is actually about. [Read more...]