  Deep learning (DL) is being increasingly utilized in healthcare-related
fields due to its outstanding efficiency. However, we have to keep the
individual health data used by DL models private and secure. Protecting data
and preserving the privacy of individuals has become an increasingly prevalent
issue. The gap between the DL and privacy communities must be bridged. In this
paper, we propose privacy-preserving deep learning (PPDL)-based approach to
secure the classification of Chest X-ray images. This study aims to use Chest
X-ray images to their fullest potential without compromising the privacy of the
data that it contains. The proposed approach is based on two steps: encrypting
the dataset using partially homomorphic encryption and training/testing the DL
algorithm over the encrypted images. Experimental results on the COVID-19
Radiography database show that the MobileNetV2 model achieves an accuracy of
94.2% over the plain data and 93.3% over the encrypted data.
