Sybil Detection Using Latent Network Structure

Grant Schoenebeck, Aaron Snook, Fang-Yi Yu


Sybil attacks, in which an adversary creates a large number of identities, present a formidable problem for the robustness of recommendation systems. One promising method of sybil detection is to use data from social network ties to implicitly infer trust.

Previous work along this dimension typically a) assumes that it is difficult/costly for an adversary to create edges to honest nodes in the network; and b) limits the amount of damage done per such edge, using conductance-based methods. However, these methods fail to detect a simple class of sybil attacks which have been identified in online systems. Indeed, conductance-based methods seem inherently unable to do so, as they are based on the assumption that creating many edges to honest nodes is difficult, which seems to fail in real-world settings.

We create a sybil defense system that accounts for the adversary's ability to launch such attacks yet provably withstands them by:


 [ back to Grant's homepage]