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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

12 Citations (Web of Science)

Abstract

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
Title of host publicationBUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH
PublisherIOS Press
Pages581-585
Number of pages5
Volume247
ISBN (Print)9781614998518
DOIs
Publication statusPublished - 2018
EventConference on Medical Informatics Europe (MIE) - SWEDEN
Duration: 24 Apr 201826 Apr 2018

Publication series

SeriesStudies in Health Technology and Informatics
Volume247
ISSN0926-9630

Conference

ConferenceConference on Medical Informatics Europe (MIE)
Period24/04/1826/04/18

Keywords

  • Infrastructure
  • machine learning
  • data mining
  • statistics
  • privacy-preserving
  • secondary use of data
  • METHODOLOGY

Cite this