Tracking bitcoin users activity using community detection on a network of weak signals

TL;DR

This paper demonstrates how community detection in Bitcoin's transaction network can partially de-anonymize users by linking multiple addresses, challenging assumptions about user privacy in cryptocurrencies.

Contribution

It introduces a novel application of complex network analysis and community detection techniques to identify user activity in Bitcoin, revealing privacy vulnerabilities.

Findings

Community detection can link multiple Bitcoin addresses to the same user.

Bitcoin transaction network exhibits community structures revealing user activity.

The method enhances understanding of privacy risks in cryptocurrency networks.

Abstract

Bitcoin is a cryptocurrency attracting a lot of interest both from the general public and researchers. There is an ongoing debate on the question of users' anonymity: while the Bitcoin protocol has been designed to ensure that the activity of individual users could not be tracked, some methods have been proposed to partially bypass this limitation. In this article, we show how the Bitcoin transaction network can be studied using complex networks analysis techniques, and in particular how community detection can be efficiently used to re-identify multiple addresses belonging to a same user.