Paper 2019/425
Homomorphic Training of 30,000 Logistic Regression Models
Flavio Bergamaschi, Shai Halevi, Tzipora T. Halevi, and Hamish Hunt
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
- Published elsewhere. Minor revision. ACNS 2019
- DOI
- 10.1007/978-3-030-21568-2_29
- Keywords
- Approximate numbersHomomorphic encryptionGWASImplementationLogistic regression
- Contact author(s)
-
thalevi @ nyu edu
flavio @ uk ibm com - History
- 2019-08-19: revised
- 2019-04-27: received
- See all versions
- Short URL
- https://ia.cr/2019/425
- License
-
CC BY
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},
doi = {10.1007/978-3-030-21568-2_29},
url = {https://eprint.iacr.org/2019/425}
}
