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Identifying Bitcoin Users Using Deep Neural Network

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Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

C. Jia—Address all correspondence related to this paper to this author. This project is partly supported the National Natural Science Foundation of China (No. 61772291), the Science Foundation of Tianjin (No. 17JCZDJC30500), the Open Project Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China(No. CAAC-ISECCA-201702).

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Author information

Authors and Affiliations

  1. College of Cyberspace Security, Nankai University, Tianjin, 300350, China

    Wei Shao, Hang Li, Mengqi Chen, Chunfu Jia & Zhi Wang

  2. Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China, Tianjin, 300300, China

    Chunbo Liu

Authors
  1. Wei Shao
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  2. Hang Li
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  3. Mengqi Chen
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  4. Chunfu Jia
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  5. Chunbo Liu
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  6. Zhi Wang
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Corresponding author

Correspondence to Chunfu Jia .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

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Shao, W., Li, H., Chen, M., Jia, C., Liu, C., Wang, Z. (2018). Identifying Bitcoin Users Using Deep Neural Network. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11337. Springer, Cham. https://doi.org/10.1007/978-3-030-05063-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-05063-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05062-7

  • Online ISBN: 978-3-030-05063-4

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