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Stud Health Technol Inform. 2019 Aug 21;264:373-377. doi: 10.3233/SHTI190246.

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

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Institute of Data Science, Maastricht University, Maastricht, The Netherlands.
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University, Medical Centre+, Maastricht, The Netherlands.
Statistics Netherlands (Centraal Bureau voor de Statistiek), Heerlen, The Netherlands.
Department of Social Medicine, CAPHRI Care and Public Health Research Institute, Maastricht University, The Netherlands.
Department of Internal Medicine, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
Department of Health, Ethics and Society, CAPHRI Research School, Maastricht University, Maastricht, The Netherlands.


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.


Data Science; Health Information Systems; Machine Learning

[Indexed for MEDLINE]

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