Abstract: Back-propagation neural network learning algorithms based on privacy preserving 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 back-propagation neural network learning algorithms don't consider how to protect the data privacy in the process of learning.This paper proposes a back propagation neural network algorithm based on privacy-preserving,which is suitable for horizontally partitioned data.In the construction process of neural networks,it is need to compute network weight vector for the training sample set.To ensure the private information of neural network learning model can not be leaked,the weight vector will be assigned to all participants,so that each participant owns a part of private values of weight vector.In the calculation of neurons,we use secure multiparty computation,thus ensuring the middle and final values of the neural network weight vector are secure and will not be leaked.Finally,the constructed learning model will be securely shared by all participants,and each participant can use the model to predict the corresponding output for their respective target data.Experimental results show that the proposed algorithm has advantages over the traditional non-privacy protection algorithm in execution time and accuracy error.

Key words: Neural network, Privacy leakage, Privacy preserving, Secure multiparty computation