Cryptology ePrint Archive: Report 2020/171

High Performance Logistic Regression for Privacy-Preserving Genome Analysis

Martine De Cock and Rafael Dowsley and Anderson C. A. Nascimento and Davis Railsback and Jianwei Shen and Ariel Todoki

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.

Category / Keywords: cryptographic protocols /

Date: received 13 Feb 2020

Contact author: mdecock at uw edu,rafael@dowsley net,andclay@uw edu,drail@uw edu,atodoki@uw edu

Available format(s): PDF | BibTeX Citation

Version: 20200214:081921 (All versions of this report)

Short URL: ia.cr/2020/171


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