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Recent studies demonstrated that X-ray radiography showed higher accuracythan Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore,applying deep learning models to X-rays and radiography images increases thespeed and accuracy of determining COVID-19 cases. However, due to HealthInsurance Portability and Accountability (HIPAA) compliance, the hospitals wereunwilling 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 evaluatethe designed model for COVID-19 detection. The EfficientNet model version wasselected according to its highest test accuracy. The injection of differentialprivacy constraints into the best-obtained model was made to evaluateperformance. The accuracy is noted by varying the trainable layers, privacyloss, and limiting information from each sample. We obtained 84\% accuracy witha privacy loss of 10 during the fine-tuning process.

Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection