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Paper 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.

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Preprint. MINOR revision.
Contact author(s)
mdecock @ uw edu,rafael @ dowsley net,andclay @ uw edu,drail @ uw edu,sjwjames @ uw edu,atodoki @ uw edu
History
2020-03-03: revised
2020-02-14: received
See all versions
Short URL
https://ia.cr/2020/171
License
Creative Commons Attribution
CC BY
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