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

2018; IOS Press; Linguagem: Inglês

10.3233/978-1-61499-852-5-581

1879-8365

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

Privacy-Preserving Technologies in Data

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.