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

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.