论文标题
在胸部X射线图像中确保COVID-19的分类：一种保护隐私的深度学习方法
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach
论文作者
论文摘要
由于其出色的效率，深度学习（DL）越来越多地在与医疗保健相关的领域中使用。但是，我们必须将DL模型使用的单个健康数据私有和安全。保护数据并保留个人的隐私已成为一个日益普遍的问题。必须弥合DL与隐私社区之间的鸿沟。在本文中，我们提出了基于隐私的深度学习（PPDL）的方法，以确保胸部X射线图像的分类。这项研究旨在将胸部X射线图像达到其最大潜力，而不会损害其包含数据的隐私。所提出的方法基于两个步骤：使用部分同质加密和训练/测试DL算法在加密图像上加密数据集。 COVID-19-19S射线照相数据库的实验结果表明，MobilenETV2模型在普通数据上的精度为94.2％，而加密数据的精度为93.3％。
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.
