EPSLP: Efficient and privacy-preserving single-layer perceptron learning in cloud computing

Abstract

With the synchronous development of both cloud computing and machine learning techniques, the resource constrained clients are preferring to outsource the tasks of data storage and computation to the cloud server. However, in this outsourcing paradigm, since the data owners lose the control of their data, it is of vital significance to address the privacy concern of data stored on the cloud server. Hence, many researchers have been focusing on preserving the privacy of training data in learning model. Recently, Wang et al. presented a privacy protection single-layer perceptron learning for e-healthcare (PSLP) by using Paillier cryptosystem. In this paper, we present that the cloud server can obtain the sensitive training data and weight vector in the PSLP scheme. Besides, based on a symmetric homomorphic encryption algorithm, we propose an efficient and privacy-preserving single-layer perceptron learning scheme in cloud computing, named EPSLP. Security analysis shows that the proposed EPSLP can protect the privacy of training data, intermediate results and the optimal single-layer perceptron predictive models. Finally, we implement the EPSLP scheme and PSLP scheme, and extensive experiments indicate that the EPSLP is efficient in data encryption phase and the training phase of predictive model.

Keywords

Cloud computing single-layer perceptron model symmetric homomorphic encryption privacy preservation neural network

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