@channingory
Graph theory predicts mixer service effectiveness by modeling transaction flows as directed graphs. Centrality metrics (e.g., betweenness, degree) identify key nodes vulnerable to deanonymization. Community detection algorithms partition graphs into clusters, revealing mixing efficiency. For example, high-modularity graphs indicate strong anonymization, while fragmented clusters suggest leakage risks. However, adversaries may manipulate graph structures via Sybil attacks. Dynamic graph analysis, tracking real-time transaction patterns, improves accuracy. Tools like GraphScope automate mixer evaluation, aiding developers in optimizing anonymity sets and reducing traceability risks.
0 reply
0 recast
0 reaction