Doing real work with FHE: The case of logistic regression

Jack L.H. Crawford, Craig Gentry, Shai Halevi, Daniel Platt, Victor Shoup

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 arbitrary functions (used for reciprocals, logarithms, etc.). Our 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 serve as a guide for what is or is not currently feasible to do with fully-homomorphic encryption.

Original languageEnglish (US)
Title of host publicationWAHC 2018 - Proceedings of the 6th Workshop on Encrypted Computing and Applied Homomorphic Cryptography, co-located with CCS 2018
PublisherAssociation for Computing Machinery
Pages1-12
Number of pages12
ISBN (Electronic)9781450359870
DOIs
StatePublished - Oct 15 2018
Event6th Annual Workshop on Encrypted Computing and Applied Homomorphic Cryptography. WAHC 208, co-located with CCS 2018 - Toronto, Canada
Duration: Oct 19 2018 → …

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Other

Other6th Annual Workshop on Encrypted Computing and Applied Homomorphic Cryptography. WAHC 208, co-located with CCS 2018
CountryCanada
CityToronto
Period10/19/18 → …

Keywords

  • Homomorphic encryption
  • Implementation
  • Logistic regression
  • Private genomic computation

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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  • Cite this

    Crawford, J. L. H., Gentry, C., Halevi, S., Platt, D., & Shoup, V. (2018). Doing real work with FHE: The case of logistic regression. In WAHC 2018 - Proceedings of the 6th Workshop on Encrypted Computing and Applied Homomorphic Cryptography, co-located with CCS 2018 (pp. 1-12). (Proceedings of the ACM Conference on Computer and Communications Security). Association for Computing Machinery. https://doi.org/10.1145/3267973.3267974