TY - GEN
T1 - Identity Discovery in Bitcoin Blockchain
T2 - 3rd International Conference on Vision, Image and Signal Processing, ICVISP 2019
AU - Christodoulou, Klitos
AU - Iosif, Elias
AU - Louca, Soulla
AU - Themistocleous, Marinos
PY - 2019/8/26
Y1 - 2019/8/26
N2 - Blockchain-based systems such as the one proposed to support the Bitcoin protocol are primarily used to enable the execution of financial transactions in a decentralized manner. The characteristics of blockchains have inspired the development of new types of applications that are shifting from its original purpose. Besides supporting the recording of crypto-currency transactions blockchains are also being exploited as mediums of recording arbitrary chunks of data. One technique for embedding such data on the public Bitcoin blockchain is using the OP_RETURN opcode creating an unspendable transaction. In this paper, we leverage data retrieved from such transactions to reveal the identity of the transacting entity. In more detail, we cast the problem of identity discovery as a classification problem. An empirical evaluation using various supervised classification models (from Naive Bayes to deep learning) yield up to 99.98% classification accuracy. In addition, it is confirmed that our feature engineering methodology on using the leading characters of the OP_RETURN instruction holds a significant discrimination power when compared against the baseline.
AB - Blockchain-based systems such as the one proposed to support the Bitcoin protocol are primarily used to enable the execution of financial transactions in a decentralized manner. The characteristics of blockchains have inspired the development of new types of applications that are shifting from its original purpose. Besides supporting the recording of crypto-currency transactions blockchains are also being exploited as mediums of recording arbitrary chunks of data. One technique for embedding such data on the public Bitcoin blockchain is using the OP_RETURN opcode creating an unspendable transaction. In this paper, we leverage data retrieved from such transactions to reveal the identity of the transacting entity. In more detail, we cast the problem of identity discovery as a classification problem. An empirical evaluation using various supervised classification models (from Naive Bayes to deep learning) yield up to 99.98% classification accuracy. In addition, it is confirmed that our feature engineering methodology on using the leading characters of the OP_RETURN instruction holds a significant discrimination power when compared against the baseline.
KW - Blockchain
KW - Classification
KW - Identity Discovery
KW - Metadata
UR - http://www.scopus.com/inward/record.url?scp=85085920504&partnerID=8YFLogxK
U2 - 10.1145/3387168.3387212
DO - 10.1145/3387168.3387212
M3 - Conference contribution
AN - SCOPUS:85085920504
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 3rd International Conference on Vision, Image and Signal Processing, ICVISP 2019
PB - Association for Computing Machinery
Y2 - 26 August 2019 through 28 August 2019
ER -