Strange bedfellows: Community identification in BitTorrent
While P2P systems benefit from large numbers of interconnected nodes, each of these connections provides an opportunity for eavesdropping. Using only the connection patterns gathered from 10,000 BitTorrent (BT) users during a one-month period, we determine whether randomized connection patterns give rise to communities of users. Even though connections in BT require not only shared interest in content, but also concurrent sessions, we find that strong communities naturally form – users inside a typical community are 5 to 25 times more likely to connect to each other than with users outside. These strong communities enable guilt by association, where the behavior of an entire community of users can be inferred by monitoring one of its members. Our study shows that through a single observation point, an attacker trying to identify such communities can uncover 50% of the network within a distance of two hops. Finally, we propose and evaluate a practical solution that mitigates this threat.