research-article

Towards Understanding and Demystifying Bitcoin Mixing Services

Authors:

Kui RenAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 33 - 44

https://doi.org/10.1145/3442381.3449880

Published: 03 June 2021 Publication History

Abstract

One reason for the popularity of Bitcoin is due to its anonymity. Although several heuristics have been used to break the anonymity, new approaches are proposed to enhance its anonymity at the same time. One of them is the mixing service. Unfortunately, mixing services have been abused to facilitate criminal activities, e.g., money laundering. As such, there is an urgent need to systematically understand Bitcoin mixing services.

In this paper, we take the first step to understand state-of-the-art Bitcoin mixing services. Specifically, we propose a generic abstraction model for mixing services and observe that there are two mixing mechanisms in the wild, i.e. swapping and obfuscating. Based on this model, we conduct a transaction-based analysis and successfully reveal the mixing mechanisms of four representative services. Besides, we propose a method to identify mixing transactions that leverage the obfuscating mechanism. The proposed approach is able to identify over 92% of the mixing transactions. Based on identified transactions, we then estimate the profit of mixing services and provide a case study of tracing the money flow of stolen Bitcoins.

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Cited By

Wang KTong MPang JWang JHan W(2024)XRAD: Ransomware Address Detection Method based on Bitcoin Transaction RelationshipsACM Transactions on the Web10.1145/3687487Online publication date: 20-Aug-2024
https://doi.org/10.1145/3687487
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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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Cited By

Wang KTong MPang JWang JHan W(2024)XRAD: Ransomware Address Detection Method based on Bitcoin Transaction RelationshipsACM Transactions on the Web10.1145/3687487Online publication date: 20-Aug-2024
https://doi.org/10.1145/3687487
Liang WLiu YYang CXie SLi KSusilo W(2024)On Identity, Transaction, and Smart Contract Privacy on Permissioned and Permissionless Blockchain: A Comprehensive SurveyACM Computing Surveys10.1145/367616456:12(1-35)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3676164
Lin DWu JYu YFu QZheng ZYang CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)DenseFlow: Spotting Cryptocurrency Money Laundering in Ethereum Transaction GraphsProceedings of the ACM Web Conference 202410.1145/3589334.3645692(4429-4438)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645692
Wahrstätter AErnstberger JYaish AZhou LQin KTsuchiya TSteinhorst SSvetinovic DChristin NBarczentewicz MGervais AChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Blockchain CensorshipProceedings of the ACM Web Conference 202410.1145/3589334.3645431(1632-1643)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645431
Wu JLin DFu QYang SChen TZheng ZSong B(2024)Toward Understanding Asset Flows in Crypto Money Laundering Through the Lenses of Ethereum HeistsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334627619(1994-2009)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3346276
Du HChe ZShen MZhu LHu J(2024)Breaking the Anonymity of Ethereum Mixing Services Using Graph Feature LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332698419(616-631)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3326984
Wahrstätter ATaudes ASvetinovic D(2024)Reducing Privacy of CoinJoin Transactions: Quantitative Bitcoin Network AnalysisIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.3353803(1-16)Online publication date: 2024
https://doi.org/10.1109/TDSC.2024.3353803
Hu XGui MCheng GLi RWu H(2024)Multi-class Bitcoin mixing service identification based on graph classificationDigital Communications and Networks10.1016/j.dcan.2024.08.010Online publication date: Aug-2024
https://doi.org/10.1016/j.dcan.2024.08.010
Shen MTang XWang WZhu LShen MTang XWang WZhu L(2024)Malicious Transaction Deanonymity in Web 3.0Security and Privacy in Web 3.010.1007/978-981-97-5752-7_8(133-153)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-981-97-5752-7_8
Nicholls JKuppa ALe‐Khac N(2024)The next phase of identifying illicit activity in BitcoinInternational Journal of Network Management10.1002/nem.2259Online publication date: 15-Jan-2024
https://doi.org/10.1002/nem.2259
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