Abstract
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 paper,
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 and show how it
can be directly applied on both unsupervised and
semi-supervised domain adaptation scenarios. Our
theoretically grounded method allows the optimization of the transportation plan and the Wasserstein
distance between the two distributions while protecting the data of both domains. We perform an
extensive series of experiments on various benchmarks (VisDA, Office-Home and Office-Caltech
datasets) that demonstrates the efficiency of our
method compared to non-private strategies