TY - GEN
T1 - Towards understanding and demystifying bitcoin mixing services
AU - Wu, Lei
AU - Hu, Yufeng
AU - Zhou, Yajin
AU - Wang, Haoyu
AU - Luo, Xiapu
AU - Wang, Zhi
AU - Zhang, Fan
AU - Ren, Kui
N1 - Funding Information:
The authors would like to thank the anonymous reviewers for their insightful comments. that helped improve the presentation of this paper. This work was partially supported by the Fundamental Research Funds for the Central Universities (No. 2020QNA5019, 2019QNA5016), Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (No. 2018R01005), the National Natural Science Foundation of China (grant No.62072046), and Hong Kong RGC Projects (No. 152193/19E, 152223/20E). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of funding agencies.
Publisher Copyright:
© 2021 ACM.
PY - 2021/4/19
Y1 - 2021/4/19
N2 - 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.
AB - 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.
KW - Anonymity
KW - Bitcoin
KW - Mixing Service
KW - Pseudonymity
UR - http://www.scopus.com/inward/record.url?scp=85107913767&partnerID=8YFLogxK
U2 - 10.1145/3442381.3449880
DO - 10.1145/3442381.3449880
M3 - Conference article published in proceeding or book
AN - SCOPUS:85107913767
T3 - The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
SP - 33
EP - 44
BT - The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PB - Association for Computing Machinery, Inc
T2 - 2021 World Wide Web Conference, WWW 2021
Y2 - 19 April 2021 through 23 April 2021
ER -
