Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data

Johan van Soest, Chang Sun, Ole Mussmann, Marco Puts, Bob van den Berg, Alexander Malic, Claudia van Oppen, David Townend, Andre Dekker, Michel Dumontier

Research output: Contribution to conferencePaperAcademic


Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data.
Original languageEnglish
Number of pages5
Publication statusPublished - 2018

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