Amaury Habrard - Differentially Private Optimal Transport: Application to Domain Adaptation

Optimal transport has received much attention during the past few years to deal with domain adaptation tasks. The goal is to transfer knowledge from a source domain to a target domain by finding a transportation of minimal cost moving the source distribution to the target one. In this work, we address the challenging task of privacy preserving domain adaptation by optimal transport. Using the Johnson-Lindenstrauss transform together with some noise, we present the first differentially private optimal transport model. Our method allows the optimization of the transportation plan and the Wasserstein distance between the two distributions while protecting the data of both domains.