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Improving Social Network-Based Sybil Defenses by Rewiring and Augmenting Social Graphs

机译:通过重新挖掘和增强社会图来改善基于社交网络的Sybil防御

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Recent solutions to defend against the Sybil attack, in which a node generates multiple identities, using social networks. In these solutions, social networks are assumed to be fast mixing, and Sybil nodes--which disrupt the fast mixing property of social networks--are detected. Little is known about the cause of the mixing quality in social graphs, and how to improve it in slow mixing ones. In this work we relate the mixing time of social graphs to graph degeneracy, which captures cohesiveness of the graph. We experimentally show that fast-mixing graphs tend to have a larger single core whereas slow-mixing graphs tend to have smaller multiple cores. We then propose several heuristics to improve the mixing of slow-mixing graphs using their topological structures by augmenting them. We show that our heuristics greatly improve Sybil defenses.
机译:最近抵御Sybil攻击的解决方案,其中节点使用社交网络生成多个身份。在这些解决方案中,假设社交网络是快速混合,而Sybil节点 - 检测到Sybil节点 - 该扰乱了社交网络的快速混合性。对社会图中的混合质量的原因,以及如何在缓慢混合的原因中熟知。在这项工作中,我们将社会图表的混合时间与图形退化进行了关系,这捕获了图形的凝聚力。我们通过实验表明快速混合图倾向于具有更大的单芯,而缓慢混合图往往具有较小的多芯。然后,我们提出了几种启发式方法,通过增强它们使用它们的拓扑结构来改善缓慢混合图的混合。我们展示我们的启发式大大改善了Sybil防御。

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