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A Bayesian approach to identify Bitcoin users

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  • Péter L Juhász
  • József Stéger
  • Dániel Kondor
  • Gábor Vattay

Abstract

Bitcoin is a digital currency and electronic payment system operating over a peer-to-peer network on the Internet. One of its most important properties is the high level of anonymity it provides for its users. The users are identified by their Bitcoin addresses, which are random strings in the public records of transactions, the blockchain. When a user initiates a Bitcoin transaction, his Bitcoin client program relays messages to other clients through the Bitcoin network. Monitoring the propagation of these messages and analyzing them carefully reveal hidden relations. In this paper, we develop a mathematical model using a probabilistic approach to link Bitcoin addresses and transactions to the originator IP address. To utilize our model, we carried out experiments by installing more than a hundred modified Bitcoin clients distributed in the network to observe as many messages as possible. During a two month observation period we were able to identify several thousand Bitcoin clients and bind their transactions to geographical locations.

Suggested Citation

  • Péter L Juhász & József Stéger & Dániel Kondor & Gábor Vattay, 2018. "A Bayesian approach to identify Bitcoin users," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    • Handle: RePEc:plo:pone00:0207000
    DOI: 10.1371/journal.pone.0207000
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    Cited by:

    1. Xu Wang & Guohua Gan & Ling-Yun Wu, 2020. "Framework and algorithms for identifying honest blocks in blockchain," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-14, January.
    2. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    3. Ahmad Firdaus & Mohd Faizal Ab Razak & Ali Feizollah & Ibrahim Abaker Targio Hashem & Mohamad Hazim & Nor Badrul Anuar, 2019. "The rise of “blockchain”: bibliometric analysis of blockchain study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1289-1331, September.
    4. Wei Sun & Alisher Tohirovich Dedahanov & Ho Young Shin & Ki Su Kim, 2020. "Switching intention to crypto-currency market: Factors predisposing some individuals to risky investment," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    5. Arindam Misra, 2024. "Tax Policy Handbook for Crypto Assets," Papers 2403.15074, arXiv.org, revised Oct 2024.
    6. Sun, Wei & Dedahanov, Alisher Tohirovich & Shin, Ho Young & Li, Wei Ping, 2021. "Factors affecting institutional investors to add crypto-currency to asset portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

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