TY - GEN
T1 - Sybil-resilient online content voting
AU - Tran, Nguyen
AU - Min, Bonan
AU - Li, Jinyang
AU - Subramanian, Lakshminarayanan
N1 - Funding Information:
We thank Alan Mislove and Krishna Gummadi for making their social network traces publicly available. We are grateful to Krishna Gummadi (our shepard), Haifeng Yu, Aditya Dhananjay, Michael Paik, Eric Hielscher, and the anonymous reviewers whose comments have helped us improve the paper. This work is supported by NSF CAREER Award CNS-0747052.
Funding Information:
Acknowledgments We thank Alan Mislove and Krishna Gummadi for making their social network traces publicly available. We are grateful to Krishna Gummadi (our shepard), Haifeng Yu, Aditya Dhananjay, Michael Paik, Eric Hielscher, and the anonymous reviewers whose comments have helped us improve the paper. This work is supported by NSF CAREER Award CNS-0747052.
PY - 2009
Y1 - 2009
N2 - 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 Sybil-resilient 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.
AB - 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 Sybil-resilient 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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M3 - Conference contribution
T3 - Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2009
SP - 15
EP - 28
BT - Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2009
PB - USENIX Association
T2 - 6th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2009
Y2 - 22 April 2009 through 24 April 2009
ER -