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.
CITATION STYLE
Möser, M., & Narayanan, A. (2022). Resurrecting Address Clustering in Bitcoin. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13411 LNCS, pp. 386–403). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-18283-9_19
Mendeley helps you to discover research relevant for your work.