PrivaTree: Collaborative Privacy-Preserving Training of Decision Trees on Biomedical Data.

Biomedical data generation and collection have become faster and more ubiquitous. Consequently, datasets are increasingly spread across hospitals, research institutions, or other entities. Exploiting such distributed datasets simultaneously can be beneficial; in particular, classification using machine learning models such as decision trees is becoming increasingly common and important.

GenoShare: Supporting Privacy-Informed Decisions for Sharing Individual-Level Genetic Data.

One major obstacle to developing precision medicine to its full potential is the privacy concerns related to genomic-data sharing. Even though the academic community has proposed many solutions to protect genomic privacy, these so far have not been adopted in practice, mainly due to their impact on the data utility. We introduce GenoShare, a framework that enables individual citizens to understand and quantify the risks of revealing genome-related privacy-sensitive attributes (e.