SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks

dc.contributor.authorYU, Haifengen_US
dc.contributor.authorGIBBONS, Phillip B.en_US
dc.contributor.authorXIAO, Fengen_US
dc.date.accessioned2008-11-20T09:21:29Zen_US
dc.date.accessioned2017-01-23T07:00:10Z
dc.date.available2008-11-20T09:21:29Zen_US
dc.date.available2017-01-23T07:00:10Z
dc.date.issued2008-02-05en_US
dc.description.abstractDecentralized distributed systems such as peer-to-peer systems are particularly vulnerable to {\em sybil attacks}, where a malicious user pretends to have multiple identities (called {\em sybil nodes}). Without a trusted central authority, defending against sybil attacks is quite challenging. Among the small number of decentralized approaches, our recent SybilGuard protocol leverages a key insight on social networks to bound the number of sybil nodes accepted. Although its direction is promising, SybilGuard can allow a large number of sybil nodes to be accepted. Furthermore, SybilGuard assumes that social networks are fast mixing, which has never been confirmed in the real world. This paper presents the novel SybilLimit protocol that leverages the same insight as SybilGuard but offers dramatically improved and near-optimal guarantees. The number of sybil nodes accepted is reduced by a factor of $\Theta(\sqrt{n})$, or around 200 times in our experiments for a million-node system. We further prove that SybilLimit's guarantee is at most a $\log n$ factor away from optimal, when considering approaches based on fast-mixing social networks. Finally, based on three large-scale real-world social networks, we provide the first evidence that real-world social networks are indeed fast mixing. This validates the fundamental assumption behind SybilLimit's and SybilGuard's approach.en_US
dc.format.extent420582 bytesen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.urihttps://dl.comp.nus.edu.sg/xmlui/handle/1900.100/2822en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTRA2/08en_US
dc.titleSybilLimit: A Near-Optimal Social Network Defense against Sybil Attacksen_US
dc.typeTechnical Reporten_US
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