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 #12 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 Sun, Chang;Ippel, Lianne;van Soest, Johan;Wouters, Birgit;Malic, Alexander;Adekunle, Onaopepo;van den Berg, Bob;Mussmann, Ole;Koster, Annemarie;van der Kallen, Carla;van Oppen, Claudia;Townend, David;Dekker, Andre;Dumontier, Michel;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190246
URL
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