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

  • Authors

    • K. H. Vani Assistant professor, Department of Computing, Coimbatore Institute of Technology, Coimbatore, Tamilnadu- 641014
    • Dr. P Balamurugan Associate professor, Department of Computer Science, Government Arts College (Autonomous), Gopalapuram, Coimbatore, ‎Tamil Nadu 641018‎
    https://doi.org/10.14419/vbn70d23

    Received date: June 12, 2025

    Accepted date: July 14, 2025

    Published date: July 24, 2025

  • Trust Evaluation Model; Multi-Cloud Storage; Fog Computing; Edge Computing; Cryptographic Key Optimization; Cloud ‎Security; Quality of Service (QoS); Privacy Preservation; Trust Management; Distributed Architecture.

  • Abstract

    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.

  • References

    1. Tricomi, G., Merlino, G., Panarello, A. and Puliafito, A., 2020. Optimal selection techniques for Cloud service providers. IEEE Access, 8, pp.203591-203618. https://doi.org/10.1109/ACCESS.2020.3035816.
    2. Khan, M.A., Khan, S.M. and Subramaniam, S.K., 2023. Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches. IEEE Access, 11, pp.140914-140933. https://doi.org/10.1109/ACCESS.2023.3339378.
    3. Angel, N.A., Ravindran, D., Vincent, P.D.R., Srinivasan, K. and Hu, Y.C., 2021. Recent advances in evolving computing paradigms: Cloud, edge, and fog technologies. Sensors, 22(1), p.196. https://doi.org/10.3390/s22010196.
    4. Kuchuk, H. and Malokhvii, E., 2024. Integration of IoT with cloud, fog, and edge computing: a review. Advanced Information Systems, 8(2), pp.65-78. https://doi.org/10.20998/2522-9052.2024.2.08.
    5. Naouri, A., Nouri, N.A., Khelloufi, A., Sada, A.B., Ning, H. and Dhelim, S., 2024. Efficient fog node placement using nature-inspired metaheuris-tic for IoT applications. Cluster Computing, 27(6), pp.8225-8241. https://doi.org/10.1007/s10586-024-04409-3.
    6. Kumar, T., Sharma, P., Cheng, X., Lalar, S., Kumar, S. and Bansal, S., 2025. Enhanced Triple Layered Approach for Mitigating Security Risks in Cloud. Computers, Materials and Continua, 83(1), pp.719-738. https://doi.org/10.32604/cmc.2025.060836.
    7. Jangjou, M. and Sohrabi, M.K., 2022. A comprehensive survey on security challenges in different network layers in cloud computing. Archives of Computational Methods in Engineering, 29(6), pp.3587-3608. https://doi.org/10.1007/s11831-022-09708-9.
    8. Abba Ari, A.A., Ngangmo, O.K., Titouna, C., Thiare, O., Mohamadou, A. and Gueroui, A.M., 2024. Enabling privacy and security in Cloud of Things: Architecture, applications, security & privacy challenges. Applied Computing and Informatics, 20(1/2), pp.119-141. https://doi.org/10.1016/j.aci.2019.11.005.
    9. Raja, V., 2024. Exploring challenges and solutions in cloud computing: A review of data security and privacy concerns. Journal of Artificial Intelli-gence General science (JAIGS) ISSN: 3006-4023, 4(1), pp.121-144. https://doi.org/10.60087/jaigs.v4i1.86.
    10. John, J. and John Singh, K., 2024. Trust value evaluation of cloud service providers using fuzzy inference based analytical process. Scientific Re-ports, 14(1), p.18028. https://doi.org/10.1038/s41598-024-69134-8.
    11. Balcão-Filho, A., Ruiz, N., de Franco Rosa, F., Bonacin, R. and Jino, M., 2021. Applying a consumer-centric framework for trust assessment of cloud computing service providers. IEEE Transactions on Services Computing, 16(1), pp.95-107. https://doi.org/10.1109/TSC.2021.3134125.
    12. Li, Y., Cao, Y., Zhuang, Y., Li, J., Du, G. and Chen, J., 2024. Blockchain-enabled trust management with location privacy preservation in ve-hicular ad hoc networks. IEEE Internet of Things Journal, 11(14), pp.24659-24671. https://doi.org/10.1109/JIOT.2024.3350694.
    13. Chen, Y.C., Yang, J.K., Yen, H.C. and Lin, P.W., 2024. Dual-Cloud Multi-Secret Sharing Architecture for Privacy Preserving Persistent Computa-tion. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2024.3436662.
    14. Sugitha, G. and S, A.L., 2024. A multi-objective privacy preservation model for cloud security using hunter prey optimization algorithm. Peer-to-Peer Networking and Applications, 17(2), pp.911-923. https://doi.org/10.1007/s12083-023-01591-w.
    15. Firouzi, F., Farahani, B. and Marinšek, A., 2022. The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT). Information Systems, 107, p.101840. https://doi.org/10.1016/j.is.2021.101840.
    16. Shirazi, S.N., Gouglidis, A., Farshad, A. and Hutchison, D., 2017. The extended cloud: Review and analysis of mobile edge computing and fog from a security and resilience perspective. IEEE Journal on Selected Areas in Communications, 35(11), pp.2586-2595. https://doi.org/10.1109/JSAC.2017.2760478.
    17. Wang, S.; Zhang, X.; Zhang, Y.; Wang, L.; Yang, J.; Wang, W. A survey on mobile edge networks: Convergence of computing, caching and com-munications. IEEE Access 2017, 5, 6757–6779. https://doi.org/10.1109/ACCESS.2017.2685434.
    18. Ren, J.; Yu, G.; He, Y.; Li, G.Y. Collaborative cloud and edge computing for latency minimization. IEEE Trans. Veh. Technol. 2019, 68, 5031–5044. https://doi.org/10.1109/TVT.2019.2904244.
    19. Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge computing: Vision and challenges. IEEE Internet Things J. 2016, 3, 637–646. https://doi.org/10.1109/JIOT.2016.2579198.
    20. Cruz, P.; Achir, N.; Viana, A.C. On the Edge of the Deployment: A Survey on Multi- Access Edge Computing. ACM Comput. Surv. (CSUR) 2022, 55, 1–34. https://doi.org/10.1145/3529758.
    21. Deshpande, S. and Ingle, R., 2018. Evidence based trust estimation model for cloud computing services. Int. J. Netw. Secur., 20(2), pp.291-303.
    22. https://www.kaggle.com/code/naifaganadily/privacypreserving-machine-learning.

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  • How to Cite

    Vani , K. H. ., & Balamurugan, D. P. . . (2025). Comparative Analysis of Trust-Based Data Storage Management Techniques in Dynamic Cloud Service. International Journal of Basic and Applied Sciences, 14(3), 258-265. https://doi.org/10.14419/vbn70d23