Recent Advances in Secure Cloud Data Storage and Retrieval Using Giant Trevally Optimizer with Quantum convolutional neural network-based Encryption Algorithm: A Systematic Review

Abstract

Cloud computing has become a cornerstone of modern information systems, offering scalable storage, distributed processing, and on-demand access to data across global networks. Its flexibility and cost efficiency have led to widespread adoption by organizations and individuals for storing large volumes of sensitive information. However, this rapid growth has introduced significant security challenges, including data breaches, unauthorized access, privacy leakage, and insider threats. Ensuring secure data storage and retrieval in cloud environments has therefore become a critical concern. Traditional encryption techniques, such as symmetric and public key cryptography, provide basic protection but are increasingly insufficient against sophisticated cyberattacks and the growing complexity of cloud infrastructures. To address these limitations, advanced approaches leveraging artificial intelligence, quantum computing, and optimization algorithms are being explored. In particular, the integration of metaheuristic optimization techniques with neural network-based encryption models has shown strong potential in enhancing cloud security. The Giant Trevally Optimizer (GTO), inspired by the hunting behavior of giant trevally fish, has emerged as an effective optimization method due to its fast convergence and ability to explore large solution spaces. When applied to cloud security, GTO can optimize key generation, encryption parameters, and resource allocation, thereby improving the efficiency, robustness, and reliability of secure cloud data storage systems.