High Performance Logistic Regression for Privacy-Preserving Genome Analysis
Cryptography and Security
2020-03-04 v2 Machine Learning
Abstract
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure Multi-Party Computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.
Cite
@article{arxiv.2002.05377,
title = {High Performance Logistic Regression for Privacy-Preserving Genome Analysis},
author = {Martine De Cock and Rafael Dowsley and Anderson C. A. Nascimento and Davis Railsback and Jianwei Shen and Ariel Todoki},
journal= {arXiv preprint arXiv:2002.05377},
year = {2020}
}