A Deep Learning-Enabled Privacy-Preserving Framework for Secure Medical Image Storage in Cloud Systems Using Advanced Convolutional Architectures

Abstract

With the rapid adoption of cloud computing for healthcare data storage, ensuring the privacy and security of sensitive medical images has become a critical challenge. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), offer new possibilities for enhancing encryption and data protection without compromising image quality or diagnostic value. This research aims to develop a state-of-the-art, CNN-based privacy-preserving framework that leverages feature extraction and adaptive encryption methods to securely manage medical imaging data in cloud environments, aligning with emerging trends in AI-driven healthcare security and regulatory compliance. With the increasing reliance on cloud-based platforms for storing and sharing medical imaging data, privacy and security concerns have become paramount. Medical images such as CT scans, MRI, and X-rays contain sensitive patient information that must be protected from unauthorized access while ensuring usability for clinical diagnosis. This research proposes a Deep Learning-Enabled Privacy-Preserving Framework that integrates advanced convolutional neural network (CNN) architectures with adaptive encryption techniques to enhance the confidentiality of medical images stored in cloud environments. The framework extracts essential features from images while applying encryption algorithms that maintain data integrity and diagnostic value. Experimental results on diverse medical image datasets demonstrate the efficiency, robustness, and scalability of the proposed approach, making it suitable for modern healthcare applications where secure, cloud-based data management is essential. The proposed method aligns with current trends in artificial intelligence, cybersecurity, and regulatory standards for medical data protection.