Towards Understanding and Demystifying Bitcoin Mixing Services

Lei Wu, Yufeng Hu, Yajin Zhou*, Haoyu Wang, Xiapu Luo, Zhi Wang, Fan Zhang, Kui Ren
Proceedings of the 30th The Web Conference (WWW 2021)


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.

    author = {Lei Wu and Yufeng Hu and Yajin Zhou and Haoyu Wang and Xiapu Luo and Zhi Wang and Fan Zhang and Kui Ren},
    title = {Towards Understanding and Demystifying Bitcoin Mixing Services},
    booktitle = {Proceedings of the 30th The Web Conference},
    year = {2021},