A Comprehensive Review of Secure Cloud Data Storage and Retrieval Using Giant Trevally Optimizer with Quantum Convolutional Neural Network-Based Encryption Algorithm

Abstract

Cloud computing has transformed modern data management by providing scalable, flexible, and cost-effective solutions for data storage and processing. However, its widespread adoption has introduced critical challenges related to data security, privacy, and secure access. Sensitive information stored in cloud environments remains vulnerable to threats such as unauthorized access, data breaches, and cryptographic weaknesses. To mitigate these risks, advanced encryption techniques and optimization-based security frameworks have gained prominence. Recent research emphasizes the integration of artificial intelligence, quantum computing, and bio-inspired optimization methods to strengthen cloud security. One notable approach is the Giant Trevally Optimizer (GTO), a metaheuristic algorithm inspired by the hunting behavior of giant trevally fish, which enables efficient optimization in areas such as resource allocation and task scheduling. Additionally, deep learning-based encryption models, particularly those using Convolutional Neural Networks (CNNs), have demonstrated strong capabilities in generating secure and adaptive encryption mechanisms. The emergence of Quantum Convolutional Neural Networks (QCNNs) further enhances these systems by leveraging quantum principles to improve computational efficiency, scalability, and overall cryptographic strength.