Dynamic Secure Multi Broad Network for Privacy Preserving of Streaming Data

Abstract

Distributed computing as a widely concerned research direction needs to use the data training model of users, making the security of users' private data become a challenging problem to be solved. At present, federated learning is the mainstream research method to solve this problem. However, federated learning is not good at distributed training on streaming data. In real scenarios, the client's data is usually continuously updated streaming data. In this paper, we propose Dynamic Secure Multi Broad Network (DSMBN), which is a novel privacy computing framework completely different from federated learning. In DSMBN, we design three interactive communication protocols to handle streaming data in different scenarios. The function of the protocol is to use random mapping to encrypt data during the interaction. The protocol ensures that the client's original data does not leave the local server when generating mapped features. The central server uses the resulting mapped features (essentially encrypted data) instead of the original data to train machine learning models. In theoretical analysis, we analyze the first protocol's security, communication costs, and computational complexity. In the experiment, we design seven experimental scenarios, including quantity balance, Non-IID data distribution and streaming data, and compare them with several mainstream privacy protection machine learning methods. The experimental results show that compared with centralized training without privacy protection, DSMBN can achieve the same test accuracy under the premise of protecting private data security. Compared with mainstream federated learning methods, DSMBN can achieve higher accuracy in the Non-IID scenarios and save computing time and communication resources.