EPPS: Efficient Privacy-Preserving Scheme in Distributed Deep Learning

As a promising training model with Neural Network, distributed deep learning has been widely applied in various scenarios, where clients and the cloud server work together only by sharing local gradients and global parameters. However, research has shown that the adversary can still reconstruct the users' private information even if little information is leaked. To address this problem, several approaches of privacy-preserving distributed training have been exploited with existing mature technologies, such as Differential Privacy, Secure Multi-party Computation and Homomorphic Encryption. However, state-of-the-art results are still defective in security, functionality and efficiency. In this paper, we propose an Efficient Privacy-Preserving Scheme (EPPS) for distributed deep learning. We claim that our solution achieves the best performance tradeoff between security, efficiency and functionality. Specifically, we adopt the threshold Paillier encryption as the underlying structure to construct our secure training model. Hence, the confidentiality of honest users' of local gradients can be guaranteed, even the cloud server colluding with multiple users. In addition, since users are often accidentally offline due to either network environment or equipment damage, our EPPS can also support users exiting at any phases of the entire work process. Further more, we conducted extensive experiments on real-world data to demonstrate the preferable performance of our proposed scheme.