Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain

• 成果类型: Article
• 署名作者: Yin, Hao Hua Sun; Langenheldt, Klaus; Harlev, Mikkel; Mukkamala, Raghava Rao; Vatrapu, Ravi
• 署名单位: Copenhagen Business School; Kristiania University College; Copenhagen Business School; Kristiania University College
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2018.1550550
• 发表日期: 2019
• 页码: 37-73
• 关键词: social media; FRAMEWORK; systems; issues; identification

摘要

Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an owning 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 de-anonymizing the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilized a sample of 957 entities (with approximate to 385 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 12 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm with default parameters, we achieve a mean cross-validation accuracy of 80.42% and F1-score of approximate to 79.64%. We show two examples, one where we predict on a set of 22 clusters that are suspected to be related to cybercriminal activities, and another where we classify 153,293 clusters to provide an estimation of the activity on the Bitcoin ecosystem. We discuss the potential applications of our method for organizational regulation and compliance, societal implications, outline study limitations, and propose future research directions. A prototype implementation of our method for organizational use is included in the appendix.