A Verifiable Privacy-preserving Machine Learning Prediction Scheme for Edge-enhanced HCPSs

Human Cyber-physical Systems (HCPSs) provide accurate and high-quality services for industry 5.0. In HCPSs, machine learning (ML) prediction provides reliable prediction results for users based on matured models, while security and privacy protection are considerable issues. Based on the modified Okamoto-Uchiyama homomorphic encryption, we propose a verifiable privacy-preserving machine learning prediction scheme for the edge-enhanced HCPSs, which outputs the verifiable prediction results for users without privacy leakage. A batch of prediction results can be verified at one time, which improves the efficiency of verification. Security analysis shows that our scheme protects the privacy of inputs, ML models and prediction results. The experiment results demonstrate that the edge computing architecture greatly alleviates the computational burden of the cloud server. Furthermore, compared with other related schemes, our scheme shows the best execution efficiency. The batch verification can optimize the performance by about 15% compared with single verification on the same scale.