Privacy-preserving AI KU Leuven
The purpose of our work is to attack the problem of securely training and exploiting AI models between several parties that are not allowed to share their data, study the possibility for several entities to combine their different private data sets to boost learning processes. Multiple parties often will not share their data for economic reasons or privacy legislation creating a need for privacy-preserving AI. Our aim is to allow ...