Privacy-Aware Adversarial Defense Approach for Medical Image Classification in Distributed Healthcare Systems

Abstract

With the rapid development of the cloud computing and Internet of Things (IoT) technologies, the massive deployment of large-scale data processing systems has become possible, especially in the healthcare field where medical image analysis is used. Deep learning models have shown impressive results in diagnostic tasks, but their application in cloud-based systems introduces key privacy and security issues, such as being susceptible to adversarial attacks. Adversarial perturbations can fool classification models, leading to misdiagnosis in medicine, while the sharing and handling of personal patient information can expose the healthcare system to privacy violations. To overcome such challenges, this paper suggests a hybrid secure inference system that combines adversarial example detection with homomorphic encryption-based privacy preservation. The proposed solution is a rather light convolutional neural network (CNN) for the detection of adversarially manipulated inputs and a denoising process to reduce the impact of perturbations prior to the classification stage. The clean or restored images are then secured by means of the CKKS homomorphic encryption scheme, which allows for computing on encrypted data without exposing sensitive information. The images are then encrypted and fed through a deep neural network to classify them in a privacy-preserving manner. Experimental results on a dataset of brain tumor images show the effectiveness of the proposed framework. The model outperforms a baseline CNN model in adversarial and clean conditions with 94.4% classification accuracy, compared with the 71.1% accuracy the baseline CNN model had under adversarial conditions. The results support that the proposed framework has succeeded in providing better adversarial robustness while preserving data privacy, which is acceptable in cloud-IoT environments for secure medical image analysis.