Bitcoin Address Clustering Method Based on Multiple Heuristic Conditions
This work addresses the need for law enforcement to track illegal transactions by enhancing Bitcoin address clustering, but it appears incremental as it builds on existing heuristics without introducing a fundamentally new approach.
The paper tackled the problem of clustering Bitcoin addresses to identify entities by analyzing transaction associations, revealing vulnerabilities in Bitcoin's anonymity. It used multiple heuristic conditions to improve address aggregation and recall rates, though specific numerical gains were not provided.
We analyzed the associations between Bitcoin transactions and addresses to cluster address and further find groups of addresses controlled by the same entity. It revealed the vulnerabilities of Bitcoin anonymity mechanism, which could be used by the law enforcement agencies to track and crack down illegal transactions. However, single heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper reviewed a variety of heuristics, and used multiple heuristics comprehensively to cluster addresses to improve the degree of address aggregation and address recall rate, which laid a foundation for further inferring of entity identity.