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The pseudonymity associated with Bitcoin has consistently sparked interest in tools and techniques designed to enhance transactional anonymity. Among the solutions developed for this purpose are Bitcoin mixing services, often called mixers, which aim to improve user privacy. Although these services can serve as mechanisms to protect financial confidentiality, their application remains contentious due to the potential misuse of illicit activities. A significant challenge for law enforcement agencies lies in the identification of Bitcoin addresses linked to suspicious activities. This study seeks to develop a dataset comprising historical addresses associated with mixing services, which can serve as training data for future research on automated detection of suspicious addresses.

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Preparing a Dataset of Mixers BTC Addresses for Machine Learning Purpose

  • 会议论文

  • 发表日期：

    2025年

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  • Przemysław Rodwald

摘要

The pseudonymity associated with Bitcoin has consistently sparked interest in tools and techniques designed to enhance transactional anonymity. Among the solutions developed for this purpose are Bitcoin mixing services, often called mixers, which aim to improve user privacy. Although these services can serve as mechanisms to protect financial confidentiality, their application remains contentious due to the potential misuse of illicit activities. A significant challenge for law enforcement agencies lies in the identification of Bitcoin addresses linked to suspicious activities. This study seeks to develop a dataset comprising historical addresses associated with mixing services, which can serve as training data for future research on automated detection of suspicious addresses.