This paper is in the following e-collection/theme issue:

Secondary Use of Clinical Data for Research and Surveillance (366) Ethics, Privacy, and Legal Issues (479)

Published on in Vol 6, No 2 (2018): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/8805, first published .
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation

Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation

Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation

Authors of this article:

Miran Kim1 Author Orcid Image ;   Yongsoo Song2, 3 Author Orcid Image ;   Shuang Wang1 Author Orcid Image ;   Yuhou Xia4 Author Orcid Image ;   Xiaoqian Jiang1 Author Orcid Image
Article Authors Cited by (143) Tweetations (2) Metrics

Miran Kim   1 , MSc, PhD ;   Yongsoo Song   2, 3 , PhD ;   Shuang Wang   1 , MSc, PhD ;   Yuhou Xia   4 , MSc ;   Xiaoqian Jiang   1 , MSc, PhD

1 Division of Biomedical Informatics, University of California, San Diego, San Diego, CA, United States

2 Department of Mathematical Sciences, Seoul National University, Seoul, Republic of Korea

3 Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, United States

4 Department of Mathematics, Princeton University, Princeton, NJ, United States

Corresponding Author:

  • Miran Kim, MSc, PhD
  • Division of Biomedical Informatics
  • University of California, San Diego
  • 9500 Gilman Drive
  • San Diego, CA, 92093
  • United States
  • Phone: 1 8582499978
  • Email: mrkim@ucsd.edu