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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”

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...

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Detalles Bibliográficos
Autor principal: Tewari, Ambuj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131640/
https://www.ncbi.nlm.nih.gov/pubmed/36995757
http://dx.doi.org/10.2196/46700
Descripción
Sumario: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.