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Paper 2020/171
High Performance Logistic Regression for Privacy-Preserving Genome Analysis
Martine De Cock and Rafael Dowsley and Anderson C. A. Nascimento and Davis Railsback and Jianwei Shen and Ariel Todoki
Abstract
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure Multi-Party Computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.
Metadata
- Available format(s)
- Category
- Cryptographic protocols
- Publication info
- Preprint. MINOR revision.
- Contact author(s)
- mdecock @ uw edu,rafael @ dowsley net,andclay @ uw edu,drail @ uw edu,sjwjames @ uw edu,atodoki @ uw edu
- History
- 2020-03-03: revised
- 2020-02-14: received
- See all versions
- Short URL
- https://ia.cr/2020/171
- License
-
CC BY