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<p>The rise of genomic sequencing raises privacy concerns due to the identifiable nature of genomic data. The GA4GH Beacon Project enables privacy-preserving data sharing but is vulnerable to membership inference attacks that reveal individual participation. Existing defenses, such as noise addition and query restrictions, rely on static policies that attackers can bypass. We introduce the first reinforcement learning (RL)-based dynamic defense for the beacon protocol, training defender and attacker agents in a multiplayer setting. Our approach adapts responses in real time, distinguishing users from adversaries and balancing privacy with utility against evolving threats.</p>

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A reinforcement learning-based approach for dynamic privacy protection in genomic data sharing beacons

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月24日

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  • Masoud Poorghaffar Aghdam,
  • Sobhan Shukueian Tabrizi,
  • Kerem Ayöz,
  • Erman Ayday,
  • Sinem Sav,
  • A. Ercüment Çiçek

摘要

The rise of genomic sequencing raises privacy concerns due to the identifiable nature of genomic data. The GA4GH Beacon Project enables privacy-preserving data sharing but is vulnerable to membership inference attacks that reveal individual participation. Existing defenses, such as noise addition and query restrictions, rely on static policies that attackers can bypass. We introduce the first reinforcement learning (RL)-based dynamic defense for the beacon protocol, training defender and attacker agents in a multiplayer setting. Our approach adapts responses in real time, distinguishing users from adversaries and balancing privacy with utility against evolving threats.