Heuristic-Based Address Clustering in Bitcoin
With the emergence of decentralized cryptocurrencies such as Bitcoin, it has become very difficult for law enforcement to detect suspicious activities, identify users and obtain transaction records for criminals who utilize the pseudoanonymity provided by the cryptocurrency system. Address clusterin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9265226/ |
| _version_ | 1818349803470323712 |
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| author | Yuhang Zhang Jun Wang Jie Luo |
| author_facet | Yuhang Zhang Jun Wang Jie Luo |
| author_sort | Yuhang Zhang |
| collection | DOAJ |
| description | With the emergence of decentralized cryptocurrencies such as Bitcoin, it has become very difficult for law enforcement to detect suspicious activities, identify users and obtain transaction records for criminals who utilize the pseudoanonymity provided by the cryptocurrency system. Address clustering aims to break such pseudoanonymity by linking addresses that are controlled by the same user based on the information available from the blockchain, such as transaction graphs. There are already two widely used heuristics for Bitcoin address clustering. One is based on the multiple input addresses of transactions. The other is based on one-time change addresses. By reconsidering the one-time change address-based heuristic from the perspective of address reuse, we propose a new heuristic that detects one-time change addresses by eliminating addresses that are reused later as non-change addresses. As a result, this heuristic works for transactions whose one-time change addresses cannot be identified by the previous two heuristics. The experimental results for different scales of Bitcoin transaction data show that the proposed heuristic has a 0.33% mean contribution to the ratio of address reduction in addition to the contribution of the multiple input addresses and one-time change address heuristics. |
| first_indexed | 2024-12-13T18:11:45Z |
| format | Article |
| id | doaj.art-6459a34f89984082bf7108090d149bfe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:11:45Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6459a34f89984082bf7108090d149bfe2022-12-21T23:35:56ZengIEEEIEEE Access2169-35362020-01-01821058221059110.1109/ACCESS.2020.30395709265226Heuristic-Based Address Clustering in BitcoinYuhang Zhang0Jun Wang1https://orcid.org/0000-0003-4173-6703Jie Luo2https://orcid.org/0000-0002-4157-9931School of Economics and Management, Beihang University, Beijing, ChinaSchool of Economics and Management, Beihang University, Beijing, ChinaState Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, ChinaWith the emergence of decentralized cryptocurrencies such as Bitcoin, it has become very difficult for law enforcement to detect suspicious activities, identify users and obtain transaction records for criminals who utilize the pseudoanonymity provided by the cryptocurrency system. Address clustering aims to break such pseudoanonymity by linking addresses that are controlled by the same user based on the information available from the blockchain, such as transaction graphs. There are already two widely used heuristics for Bitcoin address clustering. One is based on the multiple input addresses of transactions. The other is based on one-time change addresses. By reconsidering the one-time change address-based heuristic from the perspective of address reuse, we propose a new heuristic that detects one-time change addresses by eliminating addresses that are reused later as non-change addresses. As a result, this heuristic works for transactions whose one-time change addresses cannot be identified by the previous two heuristics. The experimental results for different scales of Bitcoin transaction data show that the proposed heuristic has a 0.33% mean contribution to the ratio of address reduction in addition to the contribution of the multiple input addresses and one-time change address heuristics.https://ieeexplore.ieee.org/document/9265226/Heuristicaddress clusteringblockchainBitcoin |
| spellingShingle | Yuhang Zhang Jun Wang Jie Luo Heuristic-Based Address Clustering in Bitcoin IEEE Access Heuristic address clustering blockchain Bitcoin |
| title | Heuristic-Based Address Clustering in Bitcoin |
| title_full | Heuristic-Based Address Clustering in Bitcoin |
| title_fullStr | Heuristic-Based Address Clustering in Bitcoin |
| title_full_unstemmed | Heuristic-Based Address Clustering in Bitcoin |
| title_short | Heuristic-Based Address Clustering in Bitcoin |
| title_sort | heuristic based address clustering in bitcoin |
| topic | Heuristic address clustering blockchain Bitcoin |
| url | https://ieeexplore.ieee.org/document/9265226/ |
| work_keys_str_mv | AT yuhangzhang heuristicbasedaddressclusteringinbitcoin AT junwang heuristicbasedaddressclusteringinbitcoin AT jieluo heuristicbasedaddressclusteringinbitcoin |