Comparative Analysis of Trust-Based Data Storage Management Techniques in Dynamic Cloud Service

Cloud computing (CC) has emerged as a transformative paradigm for data storage and service delivery. However, challenges in ‎trust management, data confidentiality, and quality assurance continue to limit its scalability and user adoption. To address these ‎limitations, this study proposes a Triple-layered Trust Evaluation Model (TTEM) that integrates trust computation, optimized ‎cryptographic key generation, and a layered architecture comprising edge, fog, and cloud nodes. The model dynamically ‎evaluates service providers based on multiple Quality of Service (QoS) attributes—such as bandwidth, memory, cost, and ‎reliability—using a user satisfaction score computed through weighted aggregation. Trust values are calculated at the fog layer ‎to reduce latency and ensure scalability, while secure session keys are generated using a metaheuristic-based optimizer for ‎robust cryptographic protection. The proposed TTEM is simulated and evaluated using real-world multi-cloud scenarios, ‎including datasets from Google Drive, PCloud, MEGA, and MediaFire. Comparative results show that TTEM outperforms ‎existing methods such as GA, CSA, and MSFOA in terms of trustworthiness, computational efficiency, resource utilization, ‎and data security. The model is particularly suited for dynamic, privacy-sensitive applications in smart cities, healthcare, and ‎industrial IoT environments‎.