High Performance Logistic Regression for Privacy-Preserving Genome Analysis

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

Paper 2020/171

High Performance Logistic Regression for Privacy-Preserving Genome Analysis

Martine De Cock, Rafael Dowsley, Anderson C. A. Nascimento, Davis Railsback, 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: CC BY

BibTeX

@misc{cryptoeprint:2020/171,
      author = {Martine De Cock and Rafael Dowsley and Anderson C.  A.  Nascimento and Davis Railsback and Jianwei Shen and Ariel Todoki},
      title = {High Performance Logistic Regression for Privacy-Preserving Genome Analysis},
      howpublished = {Cryptology ePrint Archive, Paper 2020/171},
      year = {2020},
      note = {\url{https://eprint.iacr.org/2020/171}},
      url = {https://eprint.iacr.org/2020/171}
}