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

Machine Learning (1501) Ethics, Privacy, and Legal Issues (494) Data Science (278) Clinical Informatics (1050) Bioinformatics Methods and Algorithms (14) Tools, Programs and Algorithms (275) Big Data (245) Electronic Health Records (1068)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26914, first published .
Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

Authors of this article:

MinDong Sung1 Author Orcid Image ;   Dongchul Cha1, 2 Author Orcid Image ;   Yu Rang Park1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

MinDong Sung   1 * , MD ;   Dongchul Cha   1, 2 * , MD ;   Yu Rang Park   1 , PhD

1 Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea

2 Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Yu Rang Park, PhD
  • Department of Biomedical Systems Informatics
  • Yonsei University College of Medicine
  • Yonsei-ro 50-1
  • Seoul, 03722
  • Republic of Korea
  • Phone: 82 2-228-2363
  • Fax: 82 2-227-8354
  • Email: yurangpark@yuhs.ac