2008 IEEE Symposium on Security and Privacy SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks

Citations in the corpus

• Mitigating Sybils in Federated Learning Poisoning (2018, background): “Many sybil detection problems involve mapping the interactions between clients into a weighted graph and using sybil defenses from social networks [36] as a reduction.”
• Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “Standard defenses [3, 114, 124, 125] against Sybil attacks [52] would apply here.”
• Proactively Accountable Anonymous Messaging in Verdict (2012, background): “Dynamic group management schemes could leverage existing Sybil prevention techniques [47, 53, 54], but we do not consider such approaches herein.”
• The socialbot network: when bots socialize for fame and money (2011, methodology): “This, however, has alarming implications on software systems that use the social graph of an OSN to provide services such as limiting Sybil nodes in distributed systems [45] and modeling trust in sociallyinformed recommender systems [43].”
• SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, methodology): “This assumption is required by all previous structurebased methods [39,38,8,26,7,36,6,14,30,33] except Íntegro [6].”
• ShadowWalker: peer-to-peer anonymous communication using redundant structured topologies (2009, background): “Recently, there has been a new line of research that uses social networks for defense against Sybil attacks [11,12,53,54].”
• On the Embeddability of Random Walk Distances (2013, background): “are resistant to noise and manipulation, they are also widely used to build robust reputation systems [17] and Sybil detection systems on social networks [43, 44].”
• Reliable Client Accounting for Hybrid Content-Distribution Networks (2012, background): “Defenses against Sybil attacks: Douceur’s original paper on the Sybil attack [17] suggests resource testing as a possible defense, and subsequent work has explored a variety of solutions for different types of resources, such as distinct physical locations [10], money [25], and social relationships ”
• SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks (2010, result): “Concurrent with the preliminary version [ 39 ] of this paper,”
• Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs (2015, methodology): “were among the first to analyze the social graph for the purpose of identifying fake accounts in OSNs [16], [17].”
• Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, methodology): “It performs worse than both SybilRank and SybilLimit, although it bounds the accepted Sybils to O(log g) per attack edge, whereg is the total number of attack edges.”
• Uncovering social network sybils in the wild (2011, methodology): “Although multiple Sybil detection algorithms have been proposed, such as SybilGuard [Yu et al. 2006], SybilLimit [Yu et al. 2008], SybilInfer [Danezis and Mittal 2009], and SumUp [Tran et al. 2009], prior work demonstrates that these Sybil detectors generalize to community detection [Viswanath et al”
• SybilInfer: Detecting Sybil Nodes using Social Networks (2009, background): “The state of the art SybilGuard [27] and SybiLimit [26] propose the use of social networks to mitigate Sybil attacks.”