Multi-input address incremental clustering for the Bitcoin blockchain based on Petri net model analysis

被引：4

作者：

Qin, Fangchi ^{[1
]}

Wu, Yan ^{[1
]}

Tao, Fang ^{[2
]}

Liu, Lu ^{[3
]}

Shi, Leilei ^{[1
]}

Miller, Anthony J. ^{[3
]}

机构：

[1] Jiangsu Univ, Sch Comp Sci & Telecommun Engn, Jiangsu Key Lab Secur Technol Ind Cyberspace, Jiangsu, Zhenjiang, Peoples R China

[2] Univ Birmingham, Birmingham Business Sch, Birmingham, England

[3] Univ Leicester, Sch Informat, Leicester, England

来源：

DIGITAL COMMUNICATIONS AND NETWORKS | 2022年 / 8卷 / 05期

关键词：

Bitcoin; Blockchain; Petri net; Incremental clustering; Input count; Output count;

D O I：

10.1016/j.dcan.2022.09.003

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Bitcoin is a cryptocurrency based on blockchain. All historical Bitcoin transactions are stored in the Bitcoin blockchain, but Bitcoin owners are generally unknown. This is the reason for Bitcoin's pseudo-anonymity, therefore it is often used for illegal transactions. Bitcoin addresses are related to Bitcoin users' identities. Some Bitcoin addresses have the potential to be analyzed due to the behavior patterns of Bitcoin transactions. However, existing Bitcoin analysis methods do not consider the fusion of new blocks' data, resulting in low efficiency of Bitcoin address analysis. In order to address this problem, this paper proposes an incremental Bitcoin address cluster method to avoid re-clustering when new block data is added. Besides, a heuristic Bitcoin address clustering algorithm is developed to improve clustering accuracy for the Bitcoin Blockchain. Experimental results show that the proposed method increases Bitcoin address cluster efficiency and accuracy.

引用

页码：680 / 686

页数：7

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