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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Feb 21, 2023
Date Accepted: Feb 22, 2023

The final, peer-reviewed published version of this preprint can be found here:

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”

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”

J Med Internet Res 2023;25:e46700

DOI: 10.2196/46700

PMID: 36995757

PMCID: 10131640

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Mhealth systems need a privacy-by-design approach: Commentary on "Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: A scoping Review"

  • Ambuj Tewari

ABSTRACT

Brauneck et al. 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: A Scoping Review”. Researchers who design mhealth systems must adopt the same privacy-by-design approach that privacy regulations such as GDPR 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.


 Citation

Please cite as:

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”

J Med Internet Res 2023;25:e46700

DOI: 10.2196/46700

PMID: 36995757

PMCID: 10131640

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