Regression Algorithm with Privacy Based on Secure Two-party Computation

doi:10.3969/j.issn.1671-1122.2018.10.002

Abstract

Abstract:

In order to ensure the accuracy of model, traditional machine learning algorithms need to collect a large amount of raw data for model training, which easily causes privacy leakage. This paper uses the additive secret sharing scheme to perform secure two-party computing on two non-collusion semi-honest servers, and gives a linear regression algorithm with privacy. Considering that the sigmoid function is hard to support secure two-party calculation, its Taylor approximation form is used, and a Logistic regression algorithm with privacy is given in combination with linear regression algorithm. Both linear regression algorithms can simultaneously achieve the privacy protection of raw data and model parameters.

Key words: secure two-party computation, secret sharing, privacy protection, regression model

CLC Number:

TP309

Chunming TANG, Weiming WEI. Regression Algorithm with Privacy Based on Secure Two-party Computation[J]. Netinfo Security, 2018, 18(10): 10-16.

Figures/Tables 2

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