Resurrecting Address Clustering in Bitcoin
This work addresses the need for more reliable blockchain analysis tools for researchers and analysts, but it is incremental as it builds on existing heuristics with better validation and optimization.
The paper tackled the problem of address clustering in Bitcoin by developing new techniques for change address detection and preventing cluster collapse, resulting in improved clustering with low false positive rates and enhanced application impact.
Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.