Version 1
: Received: 26 June 2024 / Approved: 27 June 2024 / Online: 27 June 2024 (18:36:20 CEST)
Version 2
: Received: 11 July 2024 / Approved: 12 July 2024 / Online: 12 July 2024 (23:59:04 CEST)
How to cite:
Caringella, M.; Violante, F.; De Lucci, F.; Galantucci, S.; Costantini, M. BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics. Preprints2024, 2024061956. https://doi.org/10.20944/preprints202406.1956.v2
Caringella, M.; Violante, F.; De Lucci, F.; Galantucci, S.; Costantini, M. BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics. Preprints 2024, 2024061956. https://doi.org/10.20944/preprints202406.1956.v2
Caringella, M.; Violante, F.; De Lucci, F.; Galantucci, S.; Costantini, M. BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics. Preprints2024, 2024061956. https://doi.org/10.20944/preprints202406.1956.v2
APA Style
Caringella, M., Violante, F., De Lucci, F., Galantucci, S., & Costantini, M. (2024). BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics. Preprints. https://doi.org/10.20944/preprints202406.1956.v2
Chicago/Turabian Style
Caringella, M., Stefano Galantucci and Matteo Costantini. 2024 "BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics" Preprints. https://doi.org/10.20944/preprints202406.1956.v2
Abstract
Cryptocurrencies have now become an emerging blockchain-based payment technology. Users’ identities on such networks are pseudo-anonymous in that all transactions made from an address are transparent and searchable by anyone; to preserve their privacy, users often use many different addresses. In recent years, some studies have been conducted regarding analyzing clusters of Bitcoin addresses that, according to certain heuristics, belong to the same entity. Such action allows law enforcement to have relevant information in cases where cryptocurrencies are used for illegal trafficking. Clustering methods based on a single heuristic do not allow the clustering of many addresses in a complete and accurate manner. This paper proposes Bitcoin Address Clustering based on multiple Heuristics (BACH), a tool that uses three different clustering heuristics to identify clusters of Bitcoin addresses, which are displayed through a three-dimensional graph. Based on the results, several analyses were conducted, including a comparison with Wallet Explorer, an address clustering tool similar to BACH. BACH, in addition to introducing the innovative feature of graphical visualization of the internal structure of clusters, is shown to improve address aggregation through the joint use of three heuristics for clustering.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
