The team of researchers, which includes graduate students David Choffnes (electrical engineering and computer science) and Dean Malmgren (chemical and biological engineering), and postdoctoral fellow Jordi Duch (chemical and biological engineering), studied connection patterns in the BitTorrent file-sharing network—one of the largest and most popular P2P systems today. They found that over the course of weeks, groups of users formed communities where each member consistently connected with other community members more than with users outside the community.
“This was particularly surprising because BitTorrent is designed to establish connections at random, so there is no a priori reason for such strong communities to exist,” Bustamante says. After identifying this community behavior, the researchers showed that an eavesdropper could classify users into specific communities using a relatively small number of observation points. Indeed, a savvy attacker can correctly extract communities more than 85 percent of the time by observing only 0.01 percent of the total users. Worse yet, this information could be used to launch a “guilt-by-association” attack, where an attacker need only determine the downloading behavior of one user in the community to convincingly argue that all users in the communities are doing the same.
Given the impact of this threat, the researchers developed a technique that prevents accurate classification by intelligently hiding user-intended downloading behavior in a cloud of random downloading. They showed that this approach causes an eavesdropper’s classification to be wrong the majority of the time, providing users with grounds to claim “plausible deniability” if accused.