Empirical study of privacy inference attack against deep reinforcement learning models

Abstract

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.

Keywords:

• Reinforcement learning
• privacy attack
• deep learning
• data security
• adversarial attack

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was financially supported by Systems Engineering Research Institute of China State Shipbuilding Corporation (CSSC) [Grant No. 193-A11-107-01-33].