Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent

We show how multiple data-owning parties can collabora tively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a...

Privacy-preserving dataset combination and Lasso regression for healthcare predictions

Background: Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across diferent stakehold ers and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health...