发表机构
University of Bayreuth; Iknaio Cryptoasset Analytics; Complexity Science Hub; Bavarian Central Office for the Prosecution of Cybercrime（拜罗伊特大学; Iknaio 加密资产分析; 复杂性科学中心; 巴伐利亚网络犯罪起诉中央办公室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究执法中比特币地址聚类的多输入启发式方法（MIH）可靠性，实现含九个指标的评估框架，应用于真实地址到实体映射。结果显示MIH在数据集层面表现较好，但评估完整集群时精度和召回率低，用于执法时要考虑其指标及实体依赖性。
AI 中文摘要
地址聚类是区块链取证中的一项重要技术，被执法部门广泛用于追踪非法加密资产流动。多输入启发式方法（MIH）是最常用的，它对可能与同一实体相关的地址进行聚类。然而，尽管被广泛采用，但MIH很少根据可靠的真实数据进行评估。我们实现了一个涵盖九个既定指标的可重复使用评估框架，并将其应用于根据法定报告义务直接从欧洲加密资产服务提供商获得的真实地址到实体的映射。当评估仅限于报告的地址时，MIH在数据集层面表现强劲，但评估完整集群的指标显示精度和召回率大幅降低，实体层面的结果进一步揭示了某些服务几乎完全失败。当基于MIH的集群用于支持刑事怀疑、初步扣押加密资产以确保日后没收或作为审判程序中的证据时，检察官和法官必须考虑该启发式方法的指标依赖性和实体依赖性可靠性。
英文摘要
Address clustering is an important technique in blockchain forensics, widely employed by law enforcement to trace illicit crypto asset flows. The multi-input heuristic (MIH), which clusters addresses potentially associated with the same entity, is the most widely used. Yet, despite its broad adoption, the MIH has rarely been evaluated against reliable ground truth data. We implement a reusable evaluation framework covering nine established metrics and apply it to ground truth address-to-entity mappings obtained directly from European crypto asset service providers under legally mandated reporting obligations. When evaluation is restricted to reported addresses, the MIH appears strong at dataset level: we observe no mergers between reported services and recover same-service address pairs with recall 0.71. However, this result is driven by one large service and ignores unlabeled addresses absorbed into full clusters. Metrics that assess the full clusters show substantially lower precision and recall (0.36 and 0.44), meaning that services are often only partially recovered or embedded in larger clusters. Entity-level results further reveal near-complete failures for some services. When MIH-based clusters are used to support criminal suspicion, preliminary seizure of crypto assets to secure later forfeiture/ confiscation, or as evidence in trial proceedings, prosecutors and judges must account for the heuristic's metric-dependent and entity-dependent reliability.
