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

Security in Digital Health (29) Ethics, Privacy, and Legal Issues (470) Statistical Methods (23)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22158, first published .
A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation

A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation

A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation

Authors of this article:

Marcel von Maltitz1 Author Orcid Image ;   Hendrik Ballhausen2, 3 Author Orcid Image ;   David Kaul4, 5 Author Orcid Image ;   Daniel F Fleischmann2, 3, 6 Author Orcid Image ;   Maximilian Niyazi2, 3 Author Orcid Image ;   Claus Belka2, 3 Author Orcid Image ;   Georg Carle1 Author Orcid Image
Article Authors Cited by (6) Tweetations (1) Metrics

Journals

  1. 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
  2. Ballhausen H, Hinske L. Federated Secure Computing. Informatics 2023;10(4):83 View
  3. Cho H, Froelicher D, Dokmai N, Nandi A, Sadhuka S, Hong M, Berger B. Privacy-Enhancing Technologies in Biomedical Data Science. Annual Review of Biomedical Data Science 2024;7(1):317 View
  4. Goelz C, Vieluf S, Ballhausen H. A Secure Median Implementation for the Federated Secure Computing Architecture. Applied Sciences 2024;14(17):7891 View
  5. Ballhausen H, Corradini S, Belka C, Bogdanov D, Boldrini L, Bono F, Goelz C, Landry G, Panza G, Parodi K, Talviste R, Tran H, Gambacorta M, Marschner S. Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study. npj Digital Medicine 2024;7(1) View