High Performance Logistic Regression for Privacy-Preserving Genome Analysis

TL;DR

This paper introduces a fast secure logistic regression training protocol with a novel subprotocol for privacy-preserving genome analysis, enabling efficient multi-party computation on high-dimensional data.

Contribution

It presents the fastest secure multi-party computation implementation for training logistic regression models on distributed high-dimensional genome data.

Findings

Achieved significant speedup over existing methods

Developed a new subprotocol for secure activation function computation

Demonstrated effectiveness on high-dimensional genome datasets

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.