Cascading Machine Learning to Attack Bitcoin Anonymity
This work addresses the problem of Bitcoin anonymity for law enforcement and regulatory bodies by providing an incremental improvement in entity classification.
The paper tackles the problem of characterizing entities in the Bitcoin network to address illicit activities by presenting a cascading machine learning method that uses few features from blockchain data, achieving precision close to 1.0 for each class and significantly higher accuracy compared to baselines.
Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network. In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation. Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities.