Automatic bitcoin address clustering

Dmitry Ermilov, Maxim Panov, Yury Yanovich

    Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

    69 Цитирования (Scopus)

    Аннотация

    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.

    Язык оригиналаАнглийский
    Название основной публикацииProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    РедакторыXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
    ИздательInstitute of Electrical and Electronics Engineers Inc.
    Страницы461-466
    Число страниц6
    ISBN (электронное издание)9781538614174
    DOI
    СостояниеОпубликовано - 2017
    Событие16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Мексика
    Продолжительность: 18 дек. 201721 дек. 2017

    Серия публикаций

    НазваниеProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    Том2017-December

    Конференция

    Конференция16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    Страна/TерриторияМексика
    ГородCancun
    Период18/12/1721/12/17

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