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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings

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.

Publication information

Original languageEnglish
Title of host publicationProceedings of the 51st Hawaii International Conference on System Sciences 2018
Number of pages10
Place of PublicationHonolulu
PublisherHawaii International Conference on System Sciences (HICSS)
Publication date2018
ISBN (Print)9780998133119
StatePublished - 2018
EventThe 51st Hawaii International Conference on System Sciences. HICSS 2018 - Waikoloa Village, United States
Duration: 3 Jan 20186 Jan 2018
Conference number: 51


ConferenceThe 51st Hawaii International Conference on System Sciences. HICSS 2018
LandUnited States
ByWaikoloa Village
SeriesAnnual Hawaii International Conference on System Sciences. Proceedings

    Research areas

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

ID: 55548698