Differentially Private Block Coordinate Descent for Linear Regression on Vertically Partitioned Data

We present a differentially private extension of the block coordinate descent algorithm by means of objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, we derive a utility guarantee; a tolerance parameter indicates how much the...

Accurate training of the Cox proportional hazards model on vertically-partitioned data while preserving privacy

Privacy-Preserving Contrastive Explanations with Local Foil Trees

We present the first algorithm that combines privacy-preserving technologies and state-of-the-art explainable AI to enable privacy-friendly explanations of black-box AI models. We provide a secure algorithm for contrastive explanations of black-box machine learning models that securely trains and uses local foil trees. Our work shows that the...

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...

Oncological Research on distributed Patient data: Privacy can be Preserved!

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...

CONVINCED -– Enabling privacy-preserving survival analyses using Multi-Party Computation

Integraal Kankercentrum Nederland (IKNL) maintains the Netherlands Cancer Registry (NCR). The NCR is a comprehensive, population-based registry with data on diagnosis and treatment. However, in an increasing amount of research projects, there is a need for additional data items that can be found in other databases. In particular the setting...