Based on Privacy Preserving for Back-Propagation Neural Network Learning Algorithm

Abstract

Neural network learning algorithms are widely used in medical diagnosis, bioinformatics, intrusion detection, homeland security and other fields. The common of these applications is that all of them need to extract patterns and predict trends from a large number of complex data. In these applications, how to protect the privacy of sensitive data and personal information from disclosure is an important issue. At present, the vast majority of existing neural network learning algorithms did not consider how to protect the data privacy in the process of learning. So this paper proposes two privacy-preserving back-propagation neural network protocols applied to horizontally partitioned data and vertically partitioned data separately. The two protocols are suitable for multiple participants in a distributed environment. The results of experiments show the difference of the test error rate between the proposed two protocols and the respective non-privacy preserving versions.