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Identifying Bitcoin Users Using Deep Neural Network

  • Wei Shao
  • Hang Li
  • Mengqi Chen
  • Chunfu Jia
  • Chunbo Liu
  • Zhi Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

Abstract

In Bitcoin user identification, an important challenge is to accurately link Bitcoin addresses to their owners. Previously, some heuristics based on transaction structural rules or observations were found and used for Bitcoin address clustering. In this paper, we propose a deep learning method to achieve address-user mapping. We define addresses by their transactional behaviors and seek concealed patterns and characteristics of users that can help us distinguish the owner of a certain address from millions of others.

We propose a system that learns a mapping from address representations to a compact Euclidean space where distances directly correspond to a measure of address similarity. We train a deep neural network for address behavior embedding and optimization to finally obtain an address feature vector for each address. We identify owners of addresses through address verification, recognition and clustering, where the implementation relies directly on the distance between address feature vectors.

We set up an address-user pairing dataset with extensive collections and careful sanitation. We tested our method using the dataset and proved its efficiency. In contrast to heuristic-based methods, our model shows great performance in Bitcoin user identification.

Keywords

Bitcoin Blockchain Deep learning Bitcoin privacy 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wei Shao
    • 1
  • Hang Li
    • 1
  • Mengqi Chen
    • 1
  • Chunfu Jia
    • 1
  • Chunbo Liu
    • 2
  • Zhi Wang
    • 1
  1. 1.College of Cyberspace SecurityNankai UniversityTianjinChina
  2. 2.Information Security Evaluation Center of Civil AviationCivil Aviation University of ChinaTianjinChina

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