OIDPR: Optimized Insulin Dosage based on Privacy-Preserving Reinforcement Learning

The precise dosage of insulin plays an important role in the treatment of diabetes. To offer accurate dosage, some AI-based auxiliary dosing systems have been proposed. Unfortunately, these schemes demand real-time health data, which is highly relevant to the health situation of the diabetics. The traditional personalized drug delivery frameworks for accurate dosing of insulin always collect and transmit medical data in plaintext, which may cause the disclosure of user privacy. Therefore, to optimize insulin dosage and protect privacy simultaneously, we propose a framework for an optimized insulin dosage via privacy-preserving reinforcement learning for diabetics (OIDPR). In OIDPR, both the additive secret sharing and edge computing are deployed to complete data encryption and improve efficiency. The user's medical data is divided into secret shares uniformly at random, then compute separately at the edge servers. During the computation task of Q-learning, data is stored in the format of ciphertext and processed using the proposed additive secret sharing protocol. Finally, comprehensive theoretical analyses and experiment results demonstrate the security and efficiency of our framework.