Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning

Date
2018-01-03
Authors
Harlev, Mikkel Alexander
Sun Yin, Haohua
Langenheldt, Klaus Christian
Mukkamala, Raghava
Vatrapu, Ravi
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Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity’s real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilised a sample of 434 entities (with ~ 200 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 10 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm, we achieve an accuracy of 77% and F1-score of ~ 0.75. We discuss our novel approach of Supervised Machine Learning for uncovering Bitcoin Blockchain anonymity and its potential applications to forensics and financial compliance and its societal implications, outline study limitations and propose future research directions.
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Distributed Ledger Technology, the Blockchain, Bitcoin Blockchain, Supervised Machine Learning, Classification, De-anonymization, Entity Identification
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10 pages
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Proceedings of the 51st Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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