Fine-Grained Detection for Bitcoin Mixing Services Using Heterogeneous Representation Learning

With the widespread adoption of cryptocurrencies, exemplified by Bitcoin, mixing services have been increasingly misused for illicit activities such as fraud, extortion, and money laundering. These services cut off the direct link between input and output addresses to conceal publicly available transaction traces on the blockchain, thereby evading regulatory investigations. In recent years, significant efforts have been made to deanonymize Bitcoin mixing services (BMS). While these efforts have yielded some achievements in service detection, they still face several challenges, such as vague theoretical definitions, coarse-grained identification capabilities, and insufficient labeling information. To address these challenges, this paper formalizes the notion of BMS with a syntax, security model, and goals, providing a universal definition for evaluating service anonymity and detection feasibility. We also propose a fine-grained detection framework for BMS, called BMS-FDET, which leverages heterogeneous Bitcoin transactional networks and multiple attention mechanisms to identify mixing transactions across different services. To improve the model's detection capabilities and compare performance, a sufficient ground-truth dataset with over 596,200 entries covering more than 15 mixing services is built from extensive data sources. Comprehensive experiments demonstrate the superiority of BMS-FDET over existing methods.