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Open access

Anupam Das, Nikita Borisov and Edward Chou

Abstract

The ability to track users’ activities across different websites and visits is a key tool in advertising and surveillance. The HTML5 DeviceMotion interface creates a new opportunity for such tracking via fingerprinting of smartphone motion sensors. We study the feasibility of carrying out such fingerprinting under real-world constraints and on a large scale. In particular, we collect measurements from several hundred users under realistic scenarios and show that the state-of-the-art techniques provide very low accuracy in these settings. We then improve fingerprinting accuracy by changing the classifier as well as incorporating auxiliary information. We also show how to perform fingerprinting in an open-world scenario where one must distinguish between known and previously unseen users.

We next consider the problem of developing fingerprinting countermeasures; we evaluate the usability of a previously proposed obfuscation technique and a newly developed quantization technique via a large-scale user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications.

Open access

Nikita Borisov, George Danezis and Ian Goldberg

Abstract

Users of social applications like to be notified when their friends are online. Typically, this is done by a central server keeping track of who is online and offline, as well as of all of the users’ “buddy lists”, which contain sensitive information. We present DP5, a cryptographic service that implements online presence indication in a privacy-friendly way. DP5 allows clients to register their online presence and query the presence of their list of friends while keeping this list secret. Besides presence, high-integrity status updates are supported, to facilitate key update and rendezvous protocols. While infrastructure services are required for DP5 to operate, they are designed to not require any long-term secrets and provide perfect forward secrecy in case of compromise. We provide security arguments for the indistinguishability properties of the protocol, as well as an evaluation of its scalability and performance.

Open access

Joshua Juen, Aaron Johnson, Anupam Das, Nikita Borisov and Matthew Caesar

Abstract

The Tor anonymity network has been shown vulnerable to traffic analysis attacks by autonomous systems (ASes) and Internet exchanges (IXes), which can observe different overlay hops belonging to the same circuit. We evaluate whether network path prediction techniques provide an accurate picture of the threat from such adversaries, and whether they can be used to avoid this threat. We perform a measurement study by collecting 17.2 million traceroutes from Tor relays to destinations around the Internet. We compare the collected traceroute paths to predicted paths using state-of-the-art path inference techniques. We find that traceroutes present a very different picture, with the set of ASes seen in the traceroute path differing from the predicted path 80% of the time. We also consider the impact that prediction errors have on Tor security. Using a simulator to choose paths over a week, our traceroutes indicate a user has nearly a 100% chance of at least one compromise in a week with 11% of total paths containing an AS compromise and less than 1% containing an IX compromise when using default Tor selection. We find modifying the path selection to choose paths predicted to be safe lowers total paths with an AS compromise to 0.14% but still presents a 5–11% chance of at least one compromise in a week while making 5% of paths fail, with 96% of failures due to false positives in path inferences. Our results demonstrate more measurement and better path prediction is necessary to mitigate the risk of AS and IX adversaries to Tor.