Deanonymizing Bitcoin Transactions via Network Traffic Analysis with Semi-supervised Learning
This addresses privacy vulnerabilities in Bitcoin for users and security analysts, but it is incremental as it builds on prior network layer attacks.
The paper tackles the problem of low precision in deanonymizing Bitcoin transactions by proposing NTSSL, a method combining network traffic analysis with semi-supervised learning, which achieves a 1.6 times performance improvement over existing approaches.
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.