Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning Harlev, Mikkel Alexander Sun Yin, Haohua Langenheldt, Klaus Christian Mukkamala, Raghava Vatrapu, Ravi 2017-12-28T01:52:46Z 2017-12-28T01:52:46Z 2018-01-03
dc.description.abstract 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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2018.443
dc.identifier.isbn 978-0-9981331-1-9
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Distributed Ledger Technology, the Blockchain
dc.subject Bitcoin Blockchain, Supervised Machine Learning, Classification, De-anonymization, Entity Identification
dc.title Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning
dc.type Conference Paper
dc.type.dcmi Text
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