%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 25
%N 
%P e46700
%T 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”
%A Tewari,Ambuj
%+ Department of Statistics, University of Michigan, 1085 S University Ave, Ann Arbor, MI, 48109-1107, United States, 1 734 615 0928, tewaria@umich.edu
%K mHealth
%K differential privacy
%K private synthetic data
%K federated learning
%K data protection regulation
%K data protection by design
%K privacy protection
%K General Data Protection Regulation
%K GDPR compliance
%K privacy-preserving technologies
%K secure multiparty computation
%K multiparty computation
%K machine learning
%K privacy
%D 2023
%7 30.3.2023
%9 Commentary
%J J Med Internet Res
%G English
%X Brauneck and colleagues have combined technical and legal perspectives in their timely and valuable paper “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review.” Researchers who design mobile health (mHealth) systems must adopt the same privacy-by-design approach that privacy regulations (eg, General Data Protection Regulation) do. In order to do this successfully, we will have to overcome implementation challenges in privacy-enhancing technologies such as differential privacy. We will also have to pay close attention to emerging technologies such as private synthetic data generation.
%M 36995757
%R 10.2196/46700
%U https://www.jmir.org/2023/1/e46700
%U https://doi.org/10.2196/46700
%U http://www.ncbi.nlm.nih.gov/pubmed/36995757