Location
Hilton Waikoloa Village, Hawaii
Event Website
http://hicss.hawaii.edu/
Start Date
1-3-2018
End Date
1-6-2018
Description
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.
Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning
Hilton Waikoloa Village, Hawaii
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.
https://aisel.aisnet.org/hicss-51/in/blockchain/5