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Tracking Bitcoin Users Activity Using Community Detection on a Network of Weak Signals

  • Cazabet RemyEmail author
  • Baccour Rym
  • Latapy Matthieu
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

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.

Notes

Acknowledgements

We want to thank the authors of [11], and in particular Sarah Meiklejohn, for kindly sharing with us their dataset, result of their remarkable work. This work is funded in part by the European Commission H2020 FETPROACT 2016–2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grants ANR-15-CE38-0001 (AlgoDiv) and ANR-13-CORD-0017-01 (CODDDE), by the French program PIA - Usages, services et contenus innovants" under grant O18062-44430 (REQUEST), and by the Ile-de-France program FUI21 under grant 16010629 (iTRAC).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University Lyon, UCBL, CNRS, LIRIS, UMR 5205LyonFrance
  2. 2.Sorbonne Universités, UPMC University, Paris 06, CNRS, LIP6 UMR, 7606ParisFrance

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