Sybil-Resilient Online Content Voting
نویسندگان
چکیده
Obtaining user opinion (using votes) is essential to ranking user-generated online content. However, any content voting system is susceptible to the Sybil attack where adversaries can out-vote real users by creating many Sybil identities. In this paper, we present SumUp, a Sybilresilient vote aggregation system that leverages the trust network among users to defend against Sybil attacks. SumUp uses the technique of adaptive vote flow aggregation to limit the number of bogus votes cast by adversaries to no more than the number of attack edges in the trust network (with high probability). Using user feedback on votes, SumUp further restricts the voting power of adversaries who continuously misbehave to below the number of their attack edges. Using detailed evaluation of several existing social networks (YouTube, Flickr), we show SumUp’s ability to handle Sybil attacks. By applying SumUp on the voting trace of Digg, a popular news voting site, we have found strong evidence of attack on many articles marked “popular” by Digg.
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Sybil-Resilient Online Content Rating
Obtaining user feedback (using votes) is essential in ranking user-generated online content. However, any online voting system is susceptible to the Sybil attack where adversaries can out-vote real users by creating several Sybil identities. In this paper, we present SumUp, a Sybilresilient online content rating system that leverages trust networks among users to defend against Sybil attacks wi...
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