High Performance Logistic Regression for Privacy-Preserving Genome Analysis

• URL: http://arxiv.org/abs/2002.05377v2
• Date: Tue, 3 Mar 2020 11:00:01 GMT
• Title: High Performance Logistic Regression for Privacy-Preserving Genome Analysis
• Authors: Martine De Cock and Rafael Dowsley and Anderson C. A. Nascimento and Davis Railsback and Jianwei Shen and Ariel Todoki
• Abstract summary: We present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. 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.
• Score: 15.078027648304117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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