2008
DOI: 10.1109/sp.2008.13
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SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks

Abstract: Decentralized distributed systems such as peer-to-peer systems are particularly vulnerable to sybil attacks, where a malicious user pretends to have multiple identities (called 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 [42] leverages a key insight on social networks to bound the number of sybil nodes accepted. Although its direction is promising, SybilGuard can allow… Show more

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Cited by 434 publications
(427 citation statements)
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References 27 publications
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“…Identity uniqueness. The repository defends against Sybil attacks [10] by exploiting the fact that OSNs can be used for resource testing [18,24,27]. The test in question is a Sybil attacker's ability to create and sustain acquaintances.…”
Section: Determining Whether a Host Is Spammingmentioning
confidence: 99%
See 3 more Smart Citations
“…Identity uniqueness. The repository defends against Sybil attacks [10] by exploiting the fact that OSNs can be used for resource testing [18,24,27]. The test in question is a Sybil attacker's ability to create and sustain acquaintances.…”
Section: Determining Whether a Host Is Spammingmentioning
confidence: 99%
“…The test in question is a Sybil attacker's ability to create and sustain acquaintances. Using a SybilLimit-based [27] technique ( §3.2,) the OSN provider assigns an identity uniqueness value to each node. This value quantifies the system's belief in that node not being a Sybil.…”
Section: Determining Whether a Host Is Spammingmentioning
confidence: 99%
See 2 more Smart Citations
“…The basic intuition that the creation of false identities in a social network results in suspiciously small vertex cuts has previously been explored in several papers, in the context of peer-to-peer networks [18,17] and web spam detection [2,3,6,7,13].…”
Section: Related Workmentioning
confidence: 99%