Resurrecting Address Clustering in Bitcoin
Resurrecting Address Clustering in Bitcoin
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DOI：
10.1007/978-3-031-18283-9_19
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发表时间：
2021-07
影响因子：
3.9
通讯作者：
Malte Möser;Arvind Narayanan
中科院分区：
文献类型：
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作者：
Malte Möser;Arvind Narayanan
区块链分析对于了解比特币等加密货币在实践中的使用方式至关重要，而地址集群是区块链分析的基石。然而，目前的技术依赖于没有经过严格评估或优化的启发式方法。在本文中，我们解决了变化地址识别和聚类的几个挑战。首先，我们构建了比特币区块链中已知更改的交易的基本事实集，可用于验证个人更改地址检测启发式算法的有效性。有了这个数据集，我们开发了新的技术来预测变化输出，并具有较低的假阳性率。在将我们的预测模型应用于比特币区块链之后，我们分析了由此产生的集群，并开发了检测和防止集群崩溃的方法。最后，我们评估了我们增强的集群对两个示范应用程序的影响。
Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.