Cloud computing encrypted image retrieval strategy in cloud computing using a hybrid optimization algorithm

Abstract

In today’s rapidly evolving landscape of cloud computing technologies, security and privacy have become paramount concerns, particularly in sectors like healthcare and cloud storage services. One of the most critical challenges is safeguarding sensitive data, such as images, from unauthorized access and leakage during transmission. In this context, we propose a novel framework named Hybrid Buffalo Bat based Homomorphic Convolution (HBBbHC), designed to facilitate the retrieval of source images from encrypted representations during data transmission. The technique efficiently transforms plaintext data into ciphertext, employing blockchain technology for enhanced encryption during the transfer process. We have implemented the HBBbHC method using the Python tool and rigorously evaluated its performance in terms of resource utilization, encryption and decryption efficiency, and other relevant metrics. The results demonstrate that our approach significantly enhances data transmission efficiency, thereby elevating overall system effectiveness