Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data

Peng Zhang and Maged N. Kamel Boulos ()
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Peng Zhang: Data Science Institute & Department of Computer Science, Vanderbilt University, Nashville, TN 37240, USA
Maged N. Kamel Boulos: Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal

IJERPH, 2022, vol. 19, issue 19, 1-13

Abstract: This article offers a brief overview of ‘privacy-by-design (or data-protection-by-design) research environments’, namely Trusted Research Environments (TREs, most commonly used in the United Kingdom) and Personal Health Trains (PHTs, most commonly used in mainland Europe). These secure environments are designed to enable the safe analysis of multiple, linked (and often big) data sources, including sensitive personal data and data owned by, and distributed across, different institutions. They take data protection and privacy requirements into account from the very start (conception phase, during system design) rather than as an afterthought or ‘patch’ implemented at a later stage on top of an existing environment. TREs and PHTs are becoming increasingly important for conducting large-scale privacy-preserving health research and for enabling federated learning and discoveries from big healthcare datasets. The paper also presents select examples of successful TRE and PHT implementations and of large-scale studies that used them.

Keywords: trusted research environments; personal health trains; privacy by design (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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