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

TL;DR

This paper introduces NTSSL, a semi-supervised learning approach for Bitcoin transaction deanonymization that combines network traffic analysis and transaction clustering, significantly improving accuracy over existing methods.

Contribution

The paper presents NTSSL and NTSSL+, novel semi-supervised and cross-layer methods that enhance Bitcoin deanonymization accuracy with lower costs and better performance.

Findings

Performance improved by 1.6 times over existing methods

Effective use of pseudo-labels in semi-supervised learning

Cross-layer analysis enhances deanonymization accuracy

Abstract

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.