Name
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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3267973.3267974 | IACR Cryptology ePrint Archive |
Keywords | DocType | Volume |
Homomorphic Encryption, Implementation, Logistic Regression, Private Genomic Computation | Conference | 2018 |
ISBN | Citations | PageRank |
978-1-4503-5987-0 | 4 | 0.48 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jack L. H. Crawford | 1 | 4 | 0.48 |
| Craig Gentry | 2 | 9520 | 380.03 |
| Shai Halevi | 3 | 7203 | 442.70 |
| Daniel E. Platt | 4 | 14 | 2.59 |
| Victor Shoup | 5 | 67 | 3.82 |