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

Mobile Health (mhealth) (2943) Ethics, Privacy, and Legal Issues (479) Machine Learning (1465) New Methods (945) Security and Privacy of mHealth and uHealth (124) Tools, Programs and Algorithms (266)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43664, first published .
Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks

Authors of this article:

Alexander Shen1, 2 Author Orcid Image ;   Luke Francisco1 Author Orcid Image ;   Srijan Sen3, 4 Author Orcid Image ;   Ambuj Tewari1, 5 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Tewari A. mHealth Systems Need a Privacy-by-Design Approach: Commentary on “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review”. Journal of Medical Internet Research 2023;25:e46700 View