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"

被引:3
|
作者
Tewari, Ambuj [1 ,2 ,3 ]
机构
[1] Univ Michigan, Dept Stat, Ann Arbor, MI USA
[2] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI USA
[3] Univ Michigan, Dept Stat, 1085 S Univ Ave, Ann Arbor, MI 48109 USA
关键词
mHealth; differential privacy; private synthetic data; federated learning; data protection regulation; data protection by design; privacy protection; General Data Protection Regulation; GDPR compliance; privacy-preserving technologies; secure multiparty computation; multiparty computation; machine learning; privacy;
D O I
10.2196/46700
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
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页数:3
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