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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"
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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