Privacy-Preserving Data Sharing and Computing for Outsourced Policy Iteration with Attempt Records from Multiple Users

Reinforcement learning is a machine learning framework that relies on a lot of trial-and-error processes to learn the best policy to maximize the cumulative reward through the interaction between the agent and the environment. In the actual use of this process, the computing resources possessed by a single user are limited so that the cooperation of multiple users are needed, but the joint learning of multiple users introduces the problem of privacy leakage. This research proposes a method to safely share the effort of multiple users in an encrypted state and perform the reinforcement learning with outsourcing service to reduce users calculations combined with the homomorphic properties of cryptographic algorithms and multi-key ciphertext fusion mechanism. The proposed scheme has provably security, and the experimental results show that it has an acceptable impact on performance while ensuring privacy protection.