Detecting Roles of Money Laundering in Bitcoin Mixing Transactions: A Goal Modeling and Mining Framework

Abstract

Cryptocurrency has become a new venue for money laundering. Bitcoin mixing services deliberately obfuscate the relationship between senders and recipients, making it difficult to trace suspicious money flow. We believe that the key to demystifying the bitcoin mixing services is to discover agents’ roles in the money laundering process. We propose a goal-oriented approach to modeling, discovering, and analyzing different types of roles in the agent-based business process of the bitcoin mixing scenario using historical bitcoin transaction data. It adopts the agents’ goal perspective to study the roles in the bitcoin money laundering process. Moreover, it provides a foundation to discover real-world agents’ roles in bitcoin money laundering scenarios.

The Satoshi laundromat: a review on the money laundering open door of Bitcoin mixers

“…While it has been shown that it is relatively easy to identify CoinJoin transactions (Stütz et al, 2021), a protocol commonly used in popular decentralized Bitcoin mixers, it is, to the best of my knowledge, not easy to identify the mixing service that executed an identified CoinJoin transaction. Liu et al (2021) propose a framework that could potentially be used to estimate the scale of money laundering in a Bitcoin mixing service. They identify that various categories of addresses within a mixing transaction process can be classified as different agents with their own respective goals.…”

“…Liu et al (2021) propose a framework that could potentially be used to estimate the scale of money laundering in a Bitcoin mixing service. They identify that various categories of addresses within a mixing transaction process can be classified as different agents with their own respective goals.…”

The nilcatenation problem and its application for detecting money laundering activities in cryptocurrency networks

“…Wu et al (2022) focus on the identification of addresses belonging to mixing services by analyzing transaction network patterns. Liu et al (2021a) propose a modeling framework for discovering and analyzing the roles of the agents in Bitcoin mixing services. The authors describe a case study in which their model helped in finding and characterizing the agents involved in a Bitcoin money laundering scenario.…”