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

Abstract

The rise of genomic sequencing has led to significant privacy concerns due to the sensitive and identifiable nature of genomic data. The Beacon Project, initiated by the Global Alliance for Genomics and Health (GA4GH), was designed to enable privacy-preserving sharing of genomic information via an online querying system. However, studies have revealed that the protocol is vulnerable to membership inference attacks, which can expose the presence of individuals in sensitive datasets. Existing countermeasures often degrade system utility or fail to adapt to evolving attack strategies due to their static nature. To address this, we model the interaction between the beacon and the adversary as a Stackelberg game. In this formulation, the attacker acts as the leader who selects a query strategy to maximize inference, while the defender acts as the follower who optimizes the response honesty to minimize privacy loss while maintaining utility. However, classical game-theoretic solutions are computationally intractable due to the vast search space of genomic queries. In this study, we bridge this gap by presenting a dynamic learning-based framework to approximate these equilibrium strategies. We employ a multi-agent reinforcement learning environment to solve this continuous game, training an adaptive defense policy that regulates response honesty against a sophisticated adversary capable of strategic query ordering and behavioral mimicry. Unlike conventional static defenses, this mechanism is capable of adapting in real time, dynamically differentiating between legitimate and adversarial query patterns to apply tailored policies. Consequently, this method enhances both privacy and utility, effectively countering sophisticated and evolving threats.