Predictive Security Architecture for Securing Medical Images in Cloud Based IoT
S Sangeetha, Sandeep Kumar Mathivanan, K. Deeba, Hariharan Rajadurai, Saurav Mallik · 2024
Medical image processing has been greatly impacted by the widespread adoption of IoT technology due to its resilience and ease of use across industries, especially with the introduction of cloud-based IoT devices. In modern IoT-cloud-based medicine, clinical decision-making heavily relies on patient records. However, in order to process data efficiently, the exponential growth in data production calls for sophisticated software and large-scale storage systems. In light of this, it becomes extremely important to make sure that medical images and related data are secure when being transmitted over unprotected networks. Homomorphic Encryption with Elliptic Curve Cryptography combined with Naive Bayes (HEECCN), a novel framework, is proposed to address this problem. Performance analysis shows that the HEECCN framework is more capable than traditional models, saving computation costs, query processing times, and simplifying key generation procedures. Interestingly, the suggested architecture successfully detects malicious code with an astounding accuracy rate of 91.53%, demonstrating its effectiveness in protecting patient data in IoT-cloud-based medical settings.