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

Machine Learning (1401) Ethics, Privacy, and Legal Issues (458) Security and Privacy of mHealth and uHealth (116) Secondary Use of Clinical Data for Research and Surveillance (326)

Published on in Vol 6, No 1 (2018): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/8286, first published .
Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

Authors of this article:

Md Nazmus Sadat1 Author Orcid Image ;   Xiaoqian Jiang2 Author Orcid Image ;   Md Momin Al Aziz1 Author Orcid Image ;   Shuang Wang2 Author Orcid Image ;   Noman Mohammed1 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

Journals

  1. Yang Y, Huang X, Liu X, Cheng H, Weng J, Luo X, Chang V. A Comprehensive Survey on Secure Outsourced Computation and Its Applications. IEEE Access 2019;7:159426 View
  2. Wirth F, Kussel T, Müller A, Hamacher K, Prasser F. EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation. BMC Bioinformatics 2022;23(1) View
  3. Affum E, Enchill M. Data Confidentiality in Machine Learning: Exploring Multivariate Regression and Its Application on Encrypted Medical Data. SN Computer Science 2024;5(4) View

Books/Policy Documents

  1. Devi K. Handbook of Research on Complexities, Management, and Governance in Healthcare. View