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.
|Number of pages||5|
|Publication status||Published - 2018|
van Soest, J., Sun, C., Mussmann, O., Puts, M., van den Berg, B., Malic, A., van Oppen, C., Townend, D., Dekker, A., & Dumontier, M. (2018). Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data. 581-585. https://www.ncbi.nlm.nih.gov/pubmed/29678027