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 data access across global networks. Its flexibility, cost efficiency, and scalability have led organizations and individuals to increasingly depend on cloud platforms for managing large volumes of sensitive data. However, this widespread adoption has introduced critical security concerns, including data breaches, unauthorized access, privacy leakage, and insider threats, making secure data storage and retrieval a major challenge. Traditional encryption techniques such as symmetric and asymmetric cryptography provide basic protection but are often insufficient against evolving cyber threats and the growing complexity of cloud environments. Consequently, there is a need for more advanced and intelligent security solutions. Recent developments in artificial intelligence, quantum computing, and nature-inspired optimization algorithms have created new possibilities for strengthening cloud security. In particular, integrating metaheuristic optimization techniques with advanced neural network models has shown promise. The Giant Trevally Optimizer (GTO), inspired by the hunting behavior of giant trevally fish, offers efficient search capabilities and fast convergence, making it suitable for optimizing encryption parameters, key generation, and resource allocation in secure cloud systems.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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