Unveiling Ethereum Mixing Services Using Enhanced Graph Structure Learning

Abstract

As cryptocurrency prices continue to recover, crypto crimes such as money laundering are becoming increasingly rampant. Mixing services such as Tornado Cash have become the primary tools for obfuscating illegal financial transactions due to their inherent anonymity mechanisms. Tornado Cash is a non-custodial, smart contract-based mixing service (SC-CMS) that breaks the direct mapping between deposit and withdrawal accounts, hindering regulators from tracking illicit fund flows. Existing deanonymization methods for Tornado Cash suffer from several challenges, including vague theoretical concepts, evolving mixing mechanisms, and insufficient labeled samples. To address these concerns, this paper proposes the first formal concept of SC-CMS to facilitate and evaluate the deanonymization efforts systematically. We design a novel linkability attack, LASC, based on enhanced graph structure learning, to associate mixing accounts on Tornado Cash and mathematically prove its feasibility. Comprehensive experiments on real Ethereum transactions demonstrate that LASC outperforms state-of-the-art works in both performance and efficiency.