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2018 | OriginalPaper | Chapter

Identifying Bitcoin Users Using Deep Neural Network

Authors : Wei Shao, Hang Li, Mengqi Chen, Chunfu Jia, Chunbo Liu, Zhi Wang

Published in: Algorithms and Architectures for Parallel Processing

Publisher: Springer International Publishing

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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.

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previous chapter MulAV: Multilevel and Explainable Detection of Android Malware with Data Fusion

next chapter A Practical Privacy-Preserving Face Authentication Scheme with Revocability and Reusability

Bitcoin-abe. https://github.com/bitcoin-abe/bitcoin-abe

Bitcoin blockchain info. https://blockchain.info

Bitcoin blockchain info tags. https://blockchain.info/tags

Bitcoin whos who. https://bitcoinwhoswho.com/

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Title: Identifying Bitcoin Users Using Deep Neural Network
Authors: Wei Shao
Hang Li
Mengqi Chen
Chunfu Jia
Chunbo Liu
Zhi Wang
Publisher: Springer International Publishing
Book: Algorithms and Architectures for Parallel Processing
Print ISBN: 978-3-030-05062-7

Electronic ISBN: 978-3-030-05063-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-05063-4_15

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