Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 51st Hawaii International Conference on System Sciences 2018 |
| Number of pages | 10 |
| Place of Publication | Honolulu |
| Publisher | Hawaii International Conference on System Sciences (HICSS) |
| Publication date | 2018 |
| Pages | 3497-3506 |
| ISBN (Print) | 9780998133119 |
| Publication status | Published - 2018 |
| Event | The 51st Hawaii International Conference on System Sciences. HICSS 2018 - Waikoloa Village, United States Duration: 3 Jan 2018 → 6 Jan 2018 Conference number: 51 http://www.urbanccd.org/events/2018/1/3/hawaii-international-conference-on-system-sciences-hicss-51 |
Conference
| Conference | The 51st Hawaii International Conference on System Sciences. HICSS 2018 |
|---|---|
| Number | 51 |
| Country | United States |
| City | Waikoloa Village |
| Period | 03/01/2018 → 06/01/2018 |
| Internet address |
| Series | Proceedings of the Annual Hawaii International Conference on System Sciences |
|---|---|
| ISSN | 1060-3425 |
Keywords
- Distributed ledger technology
- The Blockchain
- Bitcoin Blockchain
- Supervised machine learning
- Classification
- De-anonymization
- Entity identification
Cite this
}
Breaking Bad : De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning. / Harlev, Mikkel Alexander; Sun Yin, Haohua; Langenheldt, Klaus Christian; Mukkamala, Raghava Rao; Vatrapu, Ravi.
Proceedings of the 51st Hawaii International Conference on System Sciences 2018. Honolulu : Hawaii International Conference on System Sciences (HICSS), 2018. p. 3497-3506 (Proceedings of the Annual Hawaii International Conference on System Sciences).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Breaking Bad
T2 - De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning
AU - Harlev, Mikkel Alexander
AU - Sun Yin, Haohua
AU - Langenheldt, Klaus Christian
AU - Mukkamala, Raghava Rao
AU - Vatrapu, Ravi
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Distributed ledger technology
KW - The Blockchain
KW - Bitcoin Blockchain
KW - Supervised machine learning
KW - Classification
KW - De-anonymization
KW - Entity identification
KW - Distributed ledger technology
KW - The Blockchain
KW - Bitcoin Blockchain
KW - Supervised machine learning
KW - Classification
KW - De-anonymization
KW - Entity identification
M3 - Article in proceedings
SN - 9780998133119
SP - 3497
EP - 3506
BT - Proceedings of the 51st Hawaii International Conference on System Sciences 2018
PB - Hawaii International Conference on System Sciences (HICSS)
CY - Honolulu
ER -