  Given the increase in the use of personal data for training Deep Neural
Networks (DNNs) in tasks such as medical imaging and diagnosis, differentially
private training of DNNs is surging in importance and there is a large body of
work focusing on providing better privacy-utility trade-off. However, little
attention is given to the interpretability of these models, and how the
application of DP affects the quality of interpretations. We propose an
extensive study into the effects of DP training on DNNs, especially on medical
imaging applications, on the APTOS dataset.
