Paper 2019/425 Original: eprint.iacr.org/2019/425 ↗
Homomorphic Training of 30,000 Logistic Regression Models
Abstract
In this work, we demonstrate the use the CKKS homomorphic encryption scheme to train a large number of logistic regression models simultaneously, as needed to run a genome-wide association study (GWAS) on encrypted data. Our implementation can train more than 30,000 models (each with four features) in about 20 minutes. To that end, we rely on a similar iterative Nesterov procedure to what was used by Kim, Song, Kim, Lee, and Cheon to train a single model [KSKLC18]. We adapt this method to train many models simultaneously using the SIMD capabilities of the CKKS scheme. We also performed a thorough validation of this iterative method and evaluated its suitability both as a generic method for computing logistic regression models, and specifically for GWAS.
Metadata
- Available format(s)
PDF- Category
- Applications
- Publication info
- Preprint.
- History
- 2019-08-19: last updated
- 2019-04-27: published
- Short URL
- https://ia.cr/2019/425
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2019/425,
author = {Flavio Bergamaschi and Shai Halevi and Tzipora T. Halevi and Hamish Hunt},
title = {Homomorphic Training of 30,000 Logistic Regression Models},
howpublished = {Cryptology {ePrint} Archive, Paper 2019/425},
year = {2019},
url = {https://eprint.iacr.org/2019/425}
}