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

Mikkel Alexander Harlev, Haohua Sun Yin, Klaus Christian Langenheldt, Raghava Rao Mukkamala, Ravi Vatrapu

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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-identified 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 Blockchain anonymity and its potential applications to forensics and financial compliance and its societal implications, outline study limitations and propose future research directions.
TitelProceedings of the 51st Hawaii International Conference on System Sciences 2018
Antal sider10
ForlagHawaii International Conference on System Sciences (HICSS)
ISBN (Trykt)9780998133119
StatusUdgivet - 2018
BegivenhedThe 51st Hawaii International Conference on System Sciences. HICSS 2018 - Waikoloa Village, USA
Varighed: 3 jan. 20186 jan. 2018
Konferencens nummer: 51


KonferenceThe 51st Hawaii International Conference on System Sciences. HICSS 2018
ByWaikoloa Village
NavnProceedings of the Annual Hawaii International Conference on System Sciences


  • Distributed ledger technology
  • The Blockchain
  • Bitcoin Blockchain
  • Supervised machine learning
  • Classification
  • De-anonymization
  • Entity identification