Empirical study of privacy inference attack against deep reinforcement learning models - Huaicheng Zhou; Kanghua Mo; Teng Huang; Yongjin Li (Connection Science)
Most studies on privacy in machine learning have primarily focused on supervised learning, with little research on privacy concerns in reinforcement learning. However, our study has demonstrated that observation information can be extracted through trajectory analysis. In this paper, we propose a variable information inference attack targeting the observation space of policy models, which is categorised into two types: observed value inference attack and observed variable inference. Our algorithm has demonstrated a high success rate in privacy inference attacks for both types of observation information.