Towards Understanding and Demystifying Bitcoin Mixing Services

TL;DR

This paper systematically analyzes Bitcoin mixing services, proposing a generic model, identifying mechanisms, and developing a method to detect mixing transactions with over 92% accuracy, aiding in understanding and tracing illicit activities.

Contribution

It introduces a generic abstraction model for Bitcoin mixing services, identifies two main mechanisms, and develops a detection method with high accuracy.

Findings

Successfully identified mixing mechanisms in four services

Detected over 92% of mixing transactions

Estimated profits and traced stolen Bitcoin flows

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.