| Autor: |
Fangchi Qin, Yan Wu, Fang Tao, Lu Liu, Leilei Shi, Anthony J. Miller |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Digital Communications and Networks, Vol 8, Iss 5, Pp 680-686 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2352-8648 |
| DOI: |
10.1016/j.dcan.2022.09.003 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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