Paper 2018/202
Doing Real Work with FHE: The Case of Logistic Regression
Jack L. H. Crawford, Craig Gentry, Shai Halevi, Daniel Platt, and Victor Shoup
Abstract
We describe our recent experience, building a system that uses fully-homomorphic encryption (FHE) to approximate the coefficients of a logistic-regression model, built from genomic data. The aim of this project was to examine the feasibility of a solution that operates "deep within the bootstrapping regime," solving a problem that appears too hard to be addressed just with somewhat-homomorphic encryption. As part of this project, we implemented optimized versions of many "bread and butter" FHE tools. These tools include binary arithmetic, comparisons, partial sorting, and low-precision approximation of "complicated functions" such as reciprocals and logarithms. Our eventual solution can handle thousands of records and hundreds of fields, and it takes a few hours to run. To achieve this performance we had to be extremely frugal with expensive bootstrapping and data-movement operations. We believe that our experience in this project could server as a guide for what is or is not currently feasible to do with fully-homomorphic encryption.
Metadata
- Available format(s)
-
PDF
- Category
- Implementation
- Publication info
- Preprint. MINOR revision.
- Keywords
- Homomorphic EncryptionImplementationLogistic RegressionPrivate Genomic Computation
- Contact author(s)
- shaih @ alum mit edu
- History
- 2018-02-22: received
- Short URL
- https://ia.cr/2018/202
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2018/202,
author = {Jack L. H. Crawford and Craig Gentry and Shai Halevi and Daniel Platt and Victor Shoup},
title = {Doing Real Work with FHE: The Case of Logistic Regression},
howpublished = {Cryptology ePrint Archive, Paper 2018/202},
year = {2018},
note = {\url{https://eprint.iacr.org/2018/202}},
url = {https://eprint.iacr.org/2018/202}
}
