With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible ’Co-LivEn’ dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.