Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (22): 139-146.DOI: 10.3778/j.issn.1002-8331.2007-0337

• Network, Communication and Security • Previous Articles     Next Articles

Privacy-Preserving Linear Regression Algorithm Based on Secure Two-Party Computation

WEI Lifei, LI Mengsi, ZHANG Lei, CHEN Congcong, CHEN Yujiao, WANG Qin   

  1. College of Information, Shanghai Ocean University, Shanghai 201306, China
  • Online:2021-11-15 Published:2021-11-16



  1. 上海海洋大学 信息学院,上海 201306


With the frequent occurrence of data security and privacy leaks and the increasing scale of leaks year after year, how to ensure the privacy of data and model parameters in machine learning has aroused widespread concern in the scientific and industrial community. Aiming at the data privacy problem caused by the limitation of local storage and computing resources and the untrustworthiness of cloud platforms, this paper proposes a privacy protection linear regression algorithm for secure two-party computation based on secret sharing technology. This paper uses additive homomorphic encryption and additive masks to realize a multiplication calculation protocol of the secret shared values, and combines the mini-batch gradient descent algorithm to realize the secure linear regression algorithm on two non-collusive cloud servers. The experimental results show that the scheme protects the data and model parameters in the training and prediction phases of the linear regression algorithm at the same time, and the model prediction performance is similar to the model trained in the plaintext domain.

Key words: linear regression, secure two-party computation, secret sharing, addition homomorphic encryption, privacy protection



关键词: 线性回归, 安全两方计算, 秘密共享, 加法同态加密, 隐私保护