Paper 2020/171

High Performance Logistic Regression for Privacy-Preserving Genome Analysis

Martine De Cock, Rafael Dowsley, Anderson C. A. Nascimento, Davis Railsback, Jianwei Shen, and Ariel Todoki


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.

Available format(s)
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
2020-03-03: revised
2020-02-14: received
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Creative Commons Attribution


      author = {Martine De Cock and Rafael Dowsley and Anderson C.  A.  Nascimento and Davis Railsback and Jianwei Shen and Ariel Todoki},
      title = {High Performance Logistic Regression for Privacy-Preserving Genome Analysis},
      howpublished = {Cryptology ePrint Archive, Paper 2020/171},
      year = {2020},
      note = {\url{}},
      url = {}
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