  The aim of dataset distillation is to encode the rich features of an original
dataset into a tiny dataset. It is a promising approach to accelerate neural
network training and related studies. Different approaches have been proposed
to improve the informativeness and generalization performance of distilled
images. However, no work has comprehensively analyzed this technique from a
security perspective and there is a lack of systematic understanding of
potential risks. In this work, we conduct extensive experiments to evaluate
current state-of-the-art dataset distillation methods. We successfully use
membership inference attacks to show that privacy risks still remain. Our work
also demonstrates that dataset distillation can cause varying degrees of impact
on model robustness and amplify model unfairness across classes when making
predictions. This work offers a large-scale benchmarking framework for dataset
distillation evaluation.
