With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. However, the powerful untraceability features inherent in these services inadvertently make them attractive tools for criminals seeking to launder illicit funds. Notably, existing de-anonymization methods face three major challenges when dealing with such transactions: highly homogenized transactional semantics, limited ability to model temporal discontinuities, and insufficient consideration of structural sparsity in account association graphs. To address these, we propose GradWATCH, designed to track anonymous accounts in Ethereum privacy-preserving services. Specifically, we first design a learnable account feature mapping module to extract informative transactional semantics from raw on-chain data. We then incorporate transaction relations into the account association graph to alleviate the adverse effects of structural sparsity. To capture temporal evolution, we further propose an edge-aware sliding-window mechanism that propagates and updates gradients at three granularities. Finally, we identify accounts controlled by the same entity by measuring their embedding distances in the learned representation space. Experimental results show that even under the conditions of unbalanced labels and sparse transactions, GradWATCH still achieves significant performance gains, with relative improvements ranging from 1.62% to 15. 22% in the MRR and from 3. 85% to 7. 31% in the F_1.

翻译：随着Web 3.0技术的快速发展，公共区块链平台正涌现出旨在增强用户隐私与匿名性的新型服务。然而，这些服务固有的强大不可追踪特性，无意中使其成为犯罪分子清洗非法资金的诱人工具。值得注意的是，现有的去匿名化方法在处理此类交易时面临三大挑战：高度同质化的交易语义、对时间不连续性建模能力有限，以及对账户关联图结构稀疏性考量不足。为解决这些问题，我们提出了GradWATCH，旨在追踪以太坊隐私保护服务中的匿名账户。具体而言，我们首先设计了一个可学习的账户特征映射模块，以从原始链上数据中提取信息丰富的交易语义。随后，我们将交易关系纳入账户关联图中，以缓解结构稀疏性的不利影响。为捕捉时间演化，我们进一步提出了一种边感知滑动窗口机制，该机制在三个粒度上传播并更新梯度。最后，我们通过测量账户在学习到的表示空间中的嵌入距离，来识别由同一实体控制的账户。实验结果表明，即使在标签不平衡和交易稀疏的条件下，GradWATCH仍能取得显著的性能提升，在MRR指标上相对提升范围从1.62%到15.22%，在F_1指标上相对提升范围从3.85%到7.31%。