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A Bayesian approach to identify Bitcoin users.

Péter L Juhász^1,2, József Stéger¹, Dániel Kondor^1,3

¹Dept. of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary.

|December 14, 2018

Summary

This summary is machine-generated.

Researchers developed a probabilistic model to link Bitcoin transactions to IP addresses. Experiments revealed hidden user connections, de-anonymizing several thousand Bitcoin clients and their geographical locations.

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Area of Science:

Computer Science
Cryptography
Network Security

Background:

Bitcoin offers users a high degree of anonymity through pseudonymous Bitcoin addresses on the public blockchain.
Understanding the real-world origins of Bitcoin transactions is crucial for security and regulatory analysis.

Purpose of the Study:

To develop a mathematical model for linking Bitcoin addresses and transactions to originator IP addresses.
To investigate the feasibility of de-anonymizing Bitcoin users through network traffic analysis.

Main Methods:

A probabilistic mathematical model was developed to infer IP addresses from Bitcoin network messages.
Over one hundred modified Bitcoin clients were deployed across the network for data collection.
Transaction propagation messages were monitored and analyzed over a two-month period.

Main Results:

The study successfully linked numerous Bitcoin transactions to specific IP addresses.
Several thousand Bitcoin clients were identified and their associated transactions de-anonymized.
Geographical locations were successfully mapped to identified Bitcoin clients and their transactions.

Conclusions:

The developed model effectively compromises Bitcoin's perceived anonymity by linking transactions to IP addresses.
Network-level analysis can reveal hidden relationships and de-anonymize users in peer-to-peer systems.
This research has significant implications for Bitcoin security, privacy, and forensic investigations.