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High Performance Logistic Regression for Privacy-Preserving Genome Analysis
February 13, 2020 Β· Declared Dead Β· π BMC Medical Genomics
"No code URL or promise found in abstract"
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Authors
Martine De Cock, Rafael Dowsley, Anderson C. A. Nascimento, Davis Railsback, Jianwei Shen, Ariel Todoki
arXiv ID
2002.05377
Category
cs.CR: Cryptography & Security
Cross-listed
cs.LG
Citations
66
Venue
BMC Medical Genomics
Last Checked
5 months ago
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.
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