论文标题
COVID-19疾病检测的隐私性深度学习模型
Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
论文作者
论文摘要
最近的研究表明，X射线射线照相表现出比聚合酶链反应（PCR）测试的COVID-19检测更高的精度。因此，将深度学习模型应用于X射线和放射线照相图像增加了确定COVID-19病例的速度和准确性。但是，由于健康保险的可移植性和问责制（HIPAA），医院由于隐私问题而不愿意共享患者数据。为了维持隐私，我们提出了不同的私人深度学习模型，以保护患者的私人信息。来自Kaggle网站的数据集用于评估用于COVID-19检测的设计模型。根据其最高测试精度选择了EditivedNet模型版本。将差异隐私约束注入到最佳模型中以评估性能。通过改变可训练的层，隐私损失和限制每个样本中的信息来指出准确性。在微调过程中，我们获得了84 \％准确性，而隐私损失为10。
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.
