  Recent studies demonstrated that X-ray radiography showed higher accuracy
than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore,
applying deep learning models to X-rays and radiography images increases the
speed and accuracy of determining COVID-19 cases. However, due to Health
Insurance Portability and Accountability (HIPAA) compliance, the hospitals were
unwilling to share patient data due to privacy concerns. To maintain privacy,
we propose differential private deep learning models to secure the patients'
private information. The dataset from the Kaggle website is used to evaluate
the designed model for COVID-19 detection. The EfficientNet model version was
selected according to its highest test accuracy. The injection of differential
privacy constraints into the best-obtained model was made to evaluate
performance. The accuracy is noted by varying the trainable layers, privacy
loss, and limiting information from each sample. We obtained 84\% accuracy with
a privacy loss of 10 during the fine-tuning process.
