| Autor: |
van Soest J; Department of Radiation Oncology (MAASTRO), GROW school for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands., Sun C; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., Mussmann O; Centraal Bureau voor de Statistiek (CBS), Heerlen, The Netherlands., Puts M; Centraal Bureau voor de Statistiek (CBS), Heerlen, The Netherlands., van den Berg B; Centraal Bureau voor de Statistiek (CBS), Heerlen, The Netherlands., Malic A; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., van Oppen C; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., Towend D; Department of Health, Ethics and Society, CAPHRI Research School, Maastricht University, Maastricht, the Netherlands., Dekker A; Department of Radiation Oncology (MAASTRO), GROW school for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands., Dumontier M; Institute of Data Science, Maastricht University, Maastricht, The Netherlands. |
| Abstrakt: |
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. |
