Paper 2020/171 Original: eprint.iacr.org/2020/171 ↗
High Performance Logistic Regression for Privacy-Preserving Genome Analysis
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.
- History
- 2020-03-03: last updated
- 2020-02-14: published
- Short URL
- https://ia.cr/2020/171
- License
- https://creativecommons.org/licenses/by/4.0/
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},
url = {https://eprint.iacr.org/2020/171}
}