A Privacy-Preserving Infrastructure for Analyzing Personal Health Data in a Vertically Partitioned Scenario.

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ID: 17260
2019
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Ranked #19 of 170 articles by views in Studies in health technology and informatics

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Abstract
It is widely anticipated that the use and analysis of health-related big data will enable further understanding and improvements in human health and wellbeing. Here, we propose an innovative infrastructure, which supports secure and privacy-preserving analysis of personal health data from multiple providers with different governance policies. Our objective is to use this infrastructure to explore the relation between Type 2 Diabetes Mellitus status and healthcare costs. Our approach involves the use of distributed machine learning to analyze vertically partitioned data from the Maastricht Study, a prospective population-based cohort study, and data from the official statistics agency of the Netherlands, Statistics Netherlands (Centraal Bureau voor de Statistiek; CBS). This project seeks an optimal solution accounting for scientific, technical, and ethical/legal challenges. We describe these challenges, our progress towards addressing them in a practical use case, and a simulation experiment.
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sun2019astudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chang Sun, Lianne Ippel, Johan van Soest, Birgit Wouters, Alexander Malic, Onaopepo Adekunle, Bob van den Berg, Ole Mussmann, Annemarie Koster, Carla van der Kallen, Claudia van Oppen, David Townend, Andre Dekker, Michel Dumontier
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Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190246
URL
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