Sybil Detection via Distributed Sparse Cut Monitoring

Decentralized reputation systems help to enforce discipline and fairness in large unstructured and ad-hoc systems by rewarding good behavior and penalizing dishonest or greedy behavior. They are essential in large networks of independent nodes where centralized monitoring of node behavior is difficult due to the sheer size of the network. Sybil nodes pose a threat to the reputation systems by false referrals through sybil identities. We propose a scalable and distributed algorithm to identify attack edges and quarantine sybil clusters. This algorithm works well with dynamic trust graphs as nodes do not need to store any pre-computed data.