High Performance Logistic Regression for Privacy-Preserving Genome Analysis

2020/02/13 by Martine De Cock, Rafael Dowsley, De Cock, Martine +9
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cancer Genomics and Diagnostics #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Forensic and Genetic Research #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2002.05377

openalex publication_date 2020/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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