Privacy-Preserving Reinforcement Learning Design for Patient-Centric Dynamic Treatment Regimes

In this paper, we propose a privacy-preserving reinforcement learning framework for a patient-centric dynamic treatment regime, which we refer to as Preyer. Using Preyer, a patient-centric treatment strategy can be made spontaneously while preserving the privacy of the patient's current health state and the treatment decision. Specifically, we first design a new storage and computation method to support noninteger processing for multiple encrypted domains. A new secure plaintext length control protocol is also proposed to avoid plaintext overflow after executing secure computation repeatedly. Moreover, we design a new privacy-preserving reinforcement learning framework with experience replay to build the model for secure dynamic treatment policymaking. Furthermore, we prove that Preyer facilitates patient dynamic treatment policymaking without leaking sensitive information to unauthorized parties. We also demonstrate the utility and efficiency of Preyer using simulations and analysis.