Bitcoin Address Clustering Based on Change Address Improvement
Vedio
https://www.bilibili.com/video/BV1bv4y1t7Fx/
Abstract
Change address identification is one of the dif- ficulties in bitcoin address clustering as an emerging social computing problem. Most of the current-related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multiconditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3% more one-time change addresses than other heuristics. On top of the multi-input heuristic clustering method, the proposed method also improves the address clustering performance by 5.7%, achieves optimal recognition results compared with similar methods, and significantly reduces the false positive rate of recognition results. This work provides the technical basis for antimoney laundering efforts based on entity identification. Code and data could be accessed from https://github.com/ECNU-Cross- Innovation-Lab/BitcoinAddressClustering.
变更地址识别作为一个新兴的社会计算问题，是比特币地址聚类中的一个难点。目前的相关研究大多只适用于某些特定类型的交易，并面临着识别率低和误判率高的问题。我们创新性地提出了一种基于一次性变更地址的多条件识别的聚类方法，并对链上比特币交易数据进行了实验。结果显示，所提出的方法比其他启发式方法至少多识别了12.3%的一次性变更地址。在多输入启发式聚类方法的基础上，所提出的方法还将地址聚类性能提高了5.7%，与同类方法相比，取得了最佳的识别结果，并显著降低了识别结果的假阳性率。这项工作为基于实体识别的反洗钱工作提供了技术基础。代码和数据可以从https://github.com/ECNU-Cross- Innovation-Lab/BitcoinAddressClustering获取。