Privacy protection mechanisms are a fundamental aspect of security in cryptocurrency systems, particularly in decentralized networks such as Bitcoin. Although Bitcoin addresses are not directly associated with real-world identities, this does not fully guarantee user privacy. Various deanonymization solutions have been proposed, with network layer deanonymization attacks being especially prominent. However, existing approaches often exhibit limitations such as low precision. In this paper, we propose \textit{NTSSL}, a novel and efficient transaction deanonymization method that integrates network traffic analysis with semi-supervised learning. We use unsupervised learning algorithms to generate pseudo-labels to achieve comparable performance with lower costs. Then, we introduce \textit{NTSSL+}, a cross-layer collaborative analysis integrating transaction clustering results to further improve accuracy. Experimental results demonstrate a substantial performance improvement, 1.6 times better than the existing approach using machining learning.

翻译：隐私保护机制是加密货币系统安全性的基本方面，在诸如比特币等去中心化网络中尤其如此。尽管比特币地址并不直接与现实世界身份相关联，但这并不能完全保证用户隐私。已有多种去匿名化解决方案被提出，其中网络层去匿名化攻击尤为突出。然而，现有方法通常存在诸如精度低等局限性。本文提出一种新颖高效的交易去匿名化方法 \textit{NTSSL}，该方法将网络流量分析与半监督学习相结合。我们利用无监督学习算法生成伪标签，以较低成本实现可比的性能。进而，我们提出 \textit{NTSSL+}，这是一种集成交易聚类结果的跨层协同分析方法，旨在进一步提升准确性。实验结果表明，该方法性能显著提升，较现有基于机器学习的方法提高了1.6倍。