SybilGuard: defending against sybil attacks via social networks
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Sybil-resilient online content voting
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
An analysis of social network-based Sybil defenses
Proceedings of the ACM SIGCOMM 2010 conference
Whanau: a sybil-proof distributed hash table
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Measuring the mixing time of social graphs
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Measuring the mixing time of social graphs
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
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Social network-based Sybil defenses exploit the trust exhibited in social graphs to detect Sybil nodes that disrupt an algorithmic property (i.e., the fast mixing) in these graphs. The performance of these defenses depends on the quality of the algorithmic property and assuming a strong trust model in the underlying graph. While it is natural to think of trust value associated with the social graphs, Sybil defenses have used the social graphs without this consideration. In this paper we study paramagnetic designs to tune the performance of Sybil defenses by accounting for trust in social graphs and modeling the trust as modified random walks. Our designs are motivated by the observed relationship between the algorithmic property required for the defenses to perform well and a hypothesized trust value in the underlying graphs.