Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 61 references that are in the map. First posted on arXiv 7 Sep 2022.
Builds on
26 of 61 references.
References · 26
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14 more
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.