Value-Based Membership Inference Attack on Actor-Critic Reinforcement LearningDownload PDF

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22 Sept 2022, 12:36 (modified: 16 Nov 2022, 23:50)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: Privacy, Membership Inference Attack, Value Function, Actor-Critic, Reinforcement Learning
TL;DR: We introduce a new membership inference attack focusing on the value function of the actor-critic algorithm.
Abstract: In actor-critic reinforcement learning (RL), the so-called actor and critic, respectively, compute candidate policies and a value function that evaluates the candidate policies. Such RL algorithms may be vulnerable to membership inference attacks (MIAs), a privacy attack that infers the data membership, i.e., whether a specific data record belongs to the training dataset. We investigate the vulnerability of value function in actor-critic to MIAs. We develop \textit{CriticAttack}, a new MIA that targets black-box RL agents by examining the correlation between the expected reward and the value function. We empirically show that \textit{CriticAttack} can correctly infer approximately 90\% of the training data membership, i.e., it achieves 90\% attack accuracy. Such accuracy is far beyond the 50\% random guessing accuracy, indicating a severe privacy vulnerability of the value function. To defend against \textit{CriticAttack}, we design a method called \textit{CriticDefense} that inserts uniform noise to the value function. \textit{CriticDefense} can reduce the attack accuracy to 60\% without significantly affecting the agent’s performance.
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