Resurrecting Address Clustering in Bitcoin

Malte Möser, Arvind Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationFinancial Cryptography and Data Security - 26th International Conference, FC 2022, Revised Selected Papers
EditorsIttay Eyal, Juan Garay
PublisherSpringer Science and Business Media Deutschland GmbH
Pages386-403
Number of pages18
ISBN (Print)9783031182822
DOIs
StatePublished - 2022
Event26th International Conference on Financial Cryptography and Data Security, FC 2022 - Saint George, Grenada
Duration: May 2 2022May 6 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Financial Cryptography and Data Security, FC 2022
Country/TerritoryGrenada
CitySaint George
Period5/2/225/6/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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