Deanonymizing Bitcoin Transactions via Network Traffic Analysis with Semi-supervised Learning

Computer Science > Cryptography and Security [Submitted on 18 Mar 2026] Deanonymizing Bitcoin Transactions via Network Traffic Analysis with Semi-supervised Learning Shihan Zhang, Bing Han, Chuanyong Tian, Ruisheng Shi, Lina Lan, Qin Wang 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. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.17261 [cs.CR]   (or arXiv:2603.17261v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.17261 Focus to learn more Submission history From: Qin Wang [view email] [v1] Wed, 18 Mar 2026 01:39:26 UTC (579 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)