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.
引用
收藏
页数:3
相关论文
共 7 条
[1]   Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review [J].
Brauneck, Alissa ;
Schmalhorst, Louisa ;
Majdabadi, Mohammad Mahdi Kazemi ;
Bakhtiari, Mohammad ;
Voelker, Uwe ;
Baumbach, Jan ;
Baumbach, Linda ;
Buchholtz, Gabriele .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2023, 25
[2]  
Dwork C., 2019, Journal of Privacy and Confidentiality, P9
[3]  
He YY, 2023, Arxiv, DOI arXiv:2302.05552
[4]   Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data [J].
Liu, Jessica Chia ;
Goetz, Jack ;
Sen, Srijan ;
Tewari, Ambuj .
JMIR MHEALTH AND UHEALTH, 2021, 9 (03)
[5]  
Shen A, 2023, IN PRESS, DOI [10.2196/43664, DOI 10.2196/43664]
[6]   Statistical Data Privacy: A Song of Privacy and Utility [J].
Slavkovic, Aleksandra ;
Seeman, Jeremy .
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, 2023, 10 :189-218
[7]  
Wainwright M. J., 2014, P INT C MATH ZUR 199