A New Noise Generating Method Based on Gaussian Sampling for Privacy Preservation

Centralised machine learning brings in side effect pertaining to privacy preservation, most of machine learning methods prone to using the frameworks without privacy protection, as current methods for privacy preservation will slow down model training and testing. In order to resolve this problem, we develop a new noise generating method based on information entropy by using differential privacy for betterment the privacy protection which owns the architecture of federated machine learning. Our experiments unveil that this solution effectively preserves privacy in the vein of centralized federated learning. The gained accuracy is promising which has a room to be uplifted.