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

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Title: Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning
Authors: Harlev, Mikkel Alexander
Sun Yin, Haohua
Langenheldt, Klaus Christian
Mukkamala, Raghava
Vatrapu, Ravi
Keywords: Distributed Ledger Technology, the Blockchain
Bitcoin Blockchain, Supervised Machine Learning, Classification, De-anonymization, Entity Identification
Issue Date: 03 Jan 2018
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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/50331
ISBN: 978-0-9981331-1-9
DOI: 10.24251/HICSS.2018.443
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Distributed Ledger Technology, the Blockchain


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