Towards Understanding and Demystifying Bitcoin Mixing Services
This work addresses the need to systematically analyze Bitcoin mixing services, which are abused for criminal activities like money laundering, by providing the first step in understanding and demystifying them.
The paper tackled the problem of understanding Bitcoin mixing services by proposing a generic abstraction model and identifying two mixing mechanisms, achieving over 92% accuracy in identifying mixing transactions and enabling profit estimation and money flow tracing.
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.