TY - GEN
T1 - From Ledger to P2P Network
T2 - 15th International Symposium on Advanced Parallel Processing Technologies, APPT 2023
AU - Zheng, Che
AU - Meng, Shen
AU - Junxian, Duan
AU - Liehuang, Zhu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2023
Y1 - 2023
N2 - Cryptocurrency has the characteristics of decentralization and anonymization, which have emerged and attracted widespread attention from various parties. However, cryptocurrency anonymization breeds illegal activities such as money laundering, gambling, and phishing. Thus, it is essential to deanonymity on Cryptocurrency transactions. This paper proposes a cross-layer analysis method for Bitcoin transactions deanonymization. Through acquiring large-scale original transaction information and combining the characteristics of the network layer and the transaction layer, we propose a propagation pattern extraction model and associated address clustering model. We achieve the matching of the suspected transaction with the originator’s IP address for high precision and low overhead. Through experimental analysis in a real Bitcoin system, the cross-layer method can effectively match the original transaction with the target node, which reaches an accuracy of 81.3% and is 30% higher than the state-of-the-art method. By controlling several factors, such as different times and nodes, the characteristics of the extracted transaction propagation pattern can be proved reasonable and reliable. The practicality and effectiveness of the cross-layer analysis are higher than that of a single-level scheme.
AB - Cryptocurrency has the characteristics of decentralization and anonymization, which have emerged and attracted widespread attention from various parties. However, cryptocurrency anonymization breeds illegal activities such as money laundering, gambling, and phishing. Thus, it is essential to deanonymity on Cryptocurrency transactions. This paper proposes a cross-layer analysis method for Bitcoin transactions deanonymization. Through acquiring large-scale original transaction information and combining the characteristics of the network layer and the transaction layer, we propose a propagation pattern extraction model and associated address clustering model. We achieve the matching of the suspected transaction with the originator’s IP address for high precision and low overhead. Through experimental analysis in a real Bitcoin system, the cross-layer method can effectively match the original transaction with the target node, which reaches an accuracy of 81.3% and is 30% higher than the state-of-the-art method. By controlling several factors, such as different times and nodes, the characteristics of the extracted transaction propagation pattern can be proved reasonable and reliable. The practicality and effectiveness of the cross-layer analysis are higher than that of a single-level scheme.
KW - Address clustering
KW - Bitcoin transactions
KW - Cross-layer analysis
KW - Deanonymization
KW - Propagation path
UR - http://www.scopus.com/inward/record.url?scp=85197248669&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7872-4_22
DO - 10.1007/978-981-99-7872-4_22
M3 - Conference contribution
AN - SCOPUS:85197248669
SN - 9789819978717
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 390
EP - 416
BT - Advanced Parallel Processing Technologies - 15th International Symposium, APPT 2023, Proceedings
A2 - Li, Chao
A2 - Wu, Fan
A2 - Li, Zhenhua
A2 - Shen, Li
A2 - Gong, Xiaoli
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 August 2023 through 6 August 2023
ER -