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This paper introduces NTSSL, a novel Bitcoin transaction deanonymization method that combines network traffic analysis with semi-supervised learning to improve accuracy. Unsupervised learning generates pseudo-labels for training, reducing annotation costs while maintaining performance. NTSSL+ further enhances accuracy through cross-layer analysis integrating transaction clustering results, achieving a 1.6x improvement over existing machine learning approaches.
Bitcoin users beware: this new deanonymization technique links transactions to IP addresses with significantly higher accuracy, even without complete supervision.
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.
