Privacy preserving vertically partitioned federated learning
Abstract
Research depends on the collection of large amounts of data. However, this data is collected by different institutes, and is often difficult to share because of privacy concerns. This thesis proposes a number of new techniques that can be used to run data-analysis on data in a privacy preserving manner. It uses federated learning techniques in line with the “personal health train”-approach proposed by Dr. Andre Dekker. This thesis was part of the CARRIER project and focuses on data-sharing scenarios that are “vertically” partitioned. In such a scenario the parties sharing data have collected different types of data on the same population. For example, in the CARRIER data the data is split in a socio-economic dataset, collected by Statistics Netherlands, and medical data, collected at the MUMC and by regional GPs. All parties are collected data from the south-Limburg population. In addition to proposing new techniques, this thesis also discusses the current approach to privacy in research. It critiques the influence of large institutes, and questions if we are really protecting the data-subjects.
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