Securing Medical Data in Cloud-Based IoT Networks Using Deep Learning Techniques

The way sensitive medical data is created and sent to cloud-based platforms has been completely transformed by the growing use of Internet of Things (IoT) technology in the healthcare industry. Even with these developments, protecting the privacy and accuracy of medical records is still quite difficult. The diagnosis of diseases using medical data has become increasingly important as medical technology has advanced. It is common for medical data to move via the branches of the network from one end to the other. As a result, a high degree of security is required. Problems arise when the image data is used without authorization. One of the finest methods for securing data in an image is encryption, which uses confusion and diffusion. To address this, our study suggests a novel approach to improve medical data security by incorporating confusion and diffusion method. This feature immediately addresses issues related to unauthorized access by guaranteeing the privacy of medical records both during transmission and storage. Identifying and reducing possible security risks is greatly aided by deep learning algorithms, particularly RNN for stroke and Deep Convolutional Neural Networks (Xception model) for skin cancer photos. Our study broadens its scope to include secure and predictive analytics on medical data in the suggested system. Several metrics, including accuracy, precision, recall, confusion metrics, AUC, PSNR, and MSE, are used in this study to assess prediction models. This all-encompassing strategy improves the security of medical data while simultaneously enhancing predictive analytics' potential to improve patient care and results.