Automatic bitcoin address clustering

Dmitry Ermilov, Maxim Panov, Yury Yanovich

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    63 Citations (Scopus)

    Abstract

    Bitcoin is digital assets infrastructure powering the first worldwide decentralized cryptocurrency of the same name. All history of Bitcoins owning and transferring (addresses and transactions) is available as a public ledger called blockchain. But real-world owners of addresses are not known in general. That's why Bitcoin is called pseudo-anonymous. However, some addresses can be grouped by their ownership using behavior patterns and publicly available information from off-chain sources. Blockchain-based common behavior pattern analysis (common spending and one-time change heuristics) is widely used for Bitcoin clustering as votes for addresses association, while offchain information (tags) is mostly used to verify results. In this paper, we propose to use off-chain information as votes for address separation and to consider it together with blockchain information during the clustering model construction step. Both blockchain and off-chain information are not reliable, and our approach aims to filter out errors in input data. The results of the study show the feasibility of a proposed approached for Bitcoin address clustering. It can be useful for the users to avoid insecure Bitcoin usage patterns and for the investigators to conduct a more advanced de-anonymizing analysis.

    Original languageEnglish
    Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    EditorsXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages461-466
    Number of pages6
    ISBN (Electronic)9781538614174
    DOIs
    Publication statusPublished - 2017
    Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
    Duration: 18 Dec 201721 Dec 2017

    Publication series

    NameProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    Volume2017-December

    Conference

    Conference16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    Country/TerritoryMexico
    CityCancun
    Period18/12/1721/12/17

    Keywords

    • Bitcoin,-blockchain,-clustering,-privacy,-anonymity

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