Name
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 20min. 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 [14]. 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-21568-2_29 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Approximate numbers,Homomorphic encryption,GWAS,Implementation,Logistic regression | Homomorphic encryption,Data mining,Computer science,Iterative method,SIMD,Theoretical computer science,Encryption,Logistic regression | Journal |
Volume | ISSN | Citations |
11464 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Flávio Bergamaschi | 1 | 0 | 0.34 |
| Shai Halevi | 2 | 7203 | 442.70 |
| Tzipora Halevi | 3 | 161 | 11.97 |
| Hamish Hunt | 4 | 0 | 0.34 |