Welcome to the resource topic for 2017/979
Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption
Authors: Irene Giacomelli, Somesh Jha, Marc Joye, C. David Page, Kyonghwan YoonAbstract:
Linear regression with 2-norm regularization (i.e., ridge regression) is an important statistical technique that models the relationship between some explanatory values and an outcome value using a linear function. In many applications (e.g., predictive modelling in personalised health care), these values represent sensitive data owned by several different parties who are unwilling to share them. In this setting, training a linear regression model becomes challenging and needs specific cryptographic solutions. This problem was elegantly addressed by Nikolaenko et al. in S&P (Oakland) 2013. They suggested a two-server system that uses linearly-homomorphic encryption (LHE) and Yao’s two-party protocol (garbled circuits). In this work, we propose a novel system that can train a ridge linear regression model using only LHE (i.e., without using Yao’s protocol). This greatly improves the overall performance (both in computation and communication) as Yao’s protocol was the main bottleneck in the previous solution. The efficiency of the proposed system is validated both on synthetically-generated and real-world datasets.
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .