揭示复杂比特币混币交易：未分类案例减少67倍
Shedding Light on Complex Bitcoin Mixer Transactions: 67-Fold Reduction in Unclassified Cases
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- Moscow Institute of Physics and Technology（莫斯科物理技术学院）
- Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对比特币混币交易中因计算超时导致的1.4%未分类问题，提出四种启发式方法，将未分类率降至0.021%，实现更准确的资金流向分析。
中文摘要 AI 辅助
比特币的未花费交易输出（UTXO）模型使得资金流向的公开分析成为可能，但用户经常将交易合并到共享发送混币器（SSMs）中以掩盖这些流向。解开SSMs以恢复原始子交易是一个NP完全问题。尽管存在一种实用的解开算法，但由于计算时间限制，它无法对1.4%的SSM交易进行分类。本文引入了四种新颖的启发式方法，利用真实世界SSM交易中的结构弱点来解决这些超时案例：预emptive分组、可连接单例、模糊配对和背包回退。我们提供了验证每种启发式方法的理论证明，并将它们集成到一个优化的流程中。应用于超时交易时，我们的方法分类了98.5%先前未解决的案例，将整体未分类交易率从所有SSM交易的1.4%降至0.021%。我们在完整比特币区块链上的开源实现和全面评估表明，这些启发式方法有效地解开了先前难以处理的交易，从而实现了更准确的流向分析和对比特币交易模式更深入的结构洞察。
英文摘要
Bitcoin's Unspent Transaction Output (UTXO) model enables public analysis of fund flows, but users often merge transactions into Shared Send Mixers (SSMs) to obscure these flows. Untangling SSMs to recover original subtransactions is an NP-complete problem. While a practical untangling algorithm exists, it fails to classify 1.4% of SSM transactions due to computational time limits. This paper introduces four novel heuristics that exploit structural weaknesses in real-world SSM transactions to resolve these timeout cases: preemptive grouping, connectable singleton, ambiguous pairing, and knapsack fallback. We provide theoretical proofs validating each heuristic and integrate them into an optimized pipeline. Applied to timeout transactions, our approach classifies 98.5% of previously unresolved cases, reducing the overall unclassified transaction rate from 1.4% to 0.021% of all SSM transactions. Our open-source implementation and comprehensive evaluation on the complete Bitcoin blockchain demonstrate that the heuristics effectively untangle previously intractable transactions, enabling more accurate flow analysis and deeper structural insights into cryptocurrency transaction patterns.
