查看文章

Differential privacy (DP) is considered a de-facto standard for protecting users’ privacy in data analysis, machine, and deep learning. Existing DP-based privacy-preserving approaches, in federated learning, consist of adding noise to the clients’ gradients before sharing them with the server. However, implementing DP on the gradient is inefficient as the privacy leakage increases by increasing the synchronization training epochs due to the composition theorem. Recently, researchers were able to recover images of the training dataset using a Generative Regression Neural Network (GRNN). In this work, we propose a novel approach using two layers of privacy protection to overcome the limitations of the existing DP-based methods. The first layer leverages Hensel’s Lemma to reduce the training dataset’ s dimension. The new dimensionality reduction method reduces the dimension of a dataset without losing …