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Improving Social Network-based Sybil Defenses by Augmenting Social Graphs
| Content Provider | Semantic Scholar |
|---|---|
| Author | Mohaisen, Abedelaziz |
| Copyright Year | 2013 |
| Abstract | Many security problems in distributed systems are challenging by nature, and are caused by the lack of trust in such systems. There has been a recent direction in solving such problems by relying on the trust fabric and the wealth of algorithmic properties in social networks. For example, consider the class of attacks known as Sybil attacks, in which a node generates multiple identities with the intention to use these identities as that node is multiple nodes, are addressed using social networks: a single node in the social network can have a single identity, and is limited by the number of edges it creates with other nodes. Accordingly, detecting Sybil identities become possible when such algorithmic assumptions hold in a social network used for bootstrapping the operation of distributed systems. The mixing characteristics of social graphs, on the other hand, are the main algorithmic property used for building detection algorithms to Sybil identities in distributed systems. 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. Equipped with these findings we propose several heuristics to improve the mixing of slowmixing graphs using their topological structures by augmenting them. We finally show that our heuristics greatly improve Sybil defenses built on top of social networks for the, by accepting more honest and less Sybil nodes, even in some cases at lower cost. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.verisign.com/assets/labs/Improving_Social_Network-based_Sybil_Defenses_by_Augmenting_Social_Graphs.pdf |
| Alternate Webpage(s) | http://cs.ucf.edu/~mohaisen/doc/sybil-aug-tr.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
