Temporal Authentication Model for The Internet of Things Edge ‎Devices for Sustainable User Privacy

  • Authors

    • Jenish. M. Research Scholar, Department of Electronics and Communication Engineering, ‎RVS College of Engineering and Technology, Coimbatore
    • K. Karuppasamy K. Karuppasamy, Professor and Head, Department of Computer Science and Engineering, ‎ RVS College of Engineering and Technology, Coimbatore
    • T. Bhuvaneswari Assistant Professor, Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore
    • L. Mohana Kannan Associate Professor, Department of Biomedical Engineering, Erode Sengunthar Engineering College, Thudupathi, Erode
    • Deepika. K. Assistant Professor, Department of ECE, Karpagam Academy of Higher Education (Deemed To Be University), Coimbatore
    • S. V. Lakshmi Assistant Professor, Department of ECE, SNS College of Technology, Coimbatore
    https://doi.org/10.14419/8jbh3a60

    Received date: June 17, 2025

    Accepted date: October 15, 2025

    Published date: November 6, 2025

  • Device Authentication; Federated Learning; IoT-Edge; User Privacy

  • Abstract

    Internet of Things (IoT) integrated edge devices are used in different real-time applications for providing cloud-based services to the users. ‎As security issues are open in such integrated device platforms, the need for device authentication and user privacy is mandatory. This article, therefore, introduces a Temporal Authentication Model (TAM) for IoT-edge devices to sustain user privacy demands. The proposed ‎model employs distributed federated learning to validate authentication and revocation processes under different sharing intervals. A temporal factor is used to validate the authentication sustainability without key changes across the sharing intervals. This temporal factor is used ‎to decide the authentication or revocation for different devices. The distributed federated learning verifies the balance between these two ‎processes to ensure maximum authentication. Thus, the proposed TAM improves the authentication rate by 11.39% and reduces the revocation failure by 11.39%, authentication time by 12.44%, and complexity by 11.61% for the operation time.

  • References

    1. Halgamuge, M. N., & Niyato, D. (2025). Adaptive edge security framework for dynamic IoT security policies in diverse environments. Computers & Security, 148, 104128. https://doi.org/10.1016/j.cose.2024.104128.
    2. Pawlicki, M., Pawlicka, A., Kozik, R., & Choraś, M. (2023). The survey and meta-analysis of the attacks, transgressions, countermeasures and secu-rity aspects common to the Cloud, Edge and IoT. Neurocomputing, 551, 126533. https://doi.org/10.1016/j.neucom.2023.126533.
    3. Javadi, A., Sadeghi, S., Pahlevani, P., Bagheri, N., Rostampour, S., & Bendavid, Y. (2025). Secure and Efficient Lightweight Authentication Pro-tocol (SELAP) for multi-sector IoT applications. Internet of Things, 101499. https://doi.org/10.1016/j.iot.2025.101499.
    4. Li, S., Zhang, H., Shi, H., Ma, M., & Wang, C. (2024). A novel blockchain-enabled zero-trust-based authentication scheme in power IoT environ-ments. The Journal of Supercomputing, 80(14), 20682-20714. https://doi.org/10.1007/s11227-024-06262-y.
    5. Khan, M., Hatami, M., Zhao, W., & Chen, Y. (2024). A novel trusted hardware-based scalable security framework for IoT edge devices. Discover Internet of Things, 4(1), 4. https://doi.org/10.1007/s43926-024-00056-7.
    6. Liu, M., Lu, N., Wen, Y., Cheng, Q., & Shi, W. (2023). Sea: Secure and efficient public auditing for edge-assisted IoT aggregated data sharing. Mobile Networks and Applications, 1-12. https://doi.org/10.1007/s11036-023-02146-2.
    7. Zhonghua, C., Goyal, S. B., & Rajawat, A. S. (2024). Smart contracts attribute-based access control model for security & privacy of IoT system using blockchain and edge computing. The Journal of Supercomputing, 80(2), 1396-1425. https://doi.org/10.1007/s11227-023-05517-4.
    8. Jiang, N., Zhai, Y., Wang, Y., Yin, X., Yang, S., & Xu, P. (2024). Location Privacy Protection for the Internet of Things with Edge Computing Based on Clustering K-Anonymity. Sensors (Basel, Switzerland), 24(18), 6153. https://doi.org/10.3390/s24186153.
    9. Chen, Z., Deng, Y., Yang, M., Wu, X., Wang, X., & Wei, P. (2025). Privacy-Preserving Dynamic Spatial Keyword Query Scheme with Multi-Attribute Cost Constraints in Cloud–Edge Collaboration. Electronics, 2079-9292, 14(5). https://doi.org/10.3390/electronics14050897.
    10. Wang, J., & Li, J. (2024). Blockchain and access control encryption-empowered IoT knowledge sharing for cloud-edge orchestrated personalized privacy-preserving federated learning. Applied Sciences, 14(5), 1743. https://doi.org/10.3390/app14051743.
    11. Nakkar, M., AlTawy, R., & Youssef, A. (2022). GASE: A lightweight group authentication scheme with key agreement for edge computing appli-cations. IEEE Internet of Things Journal, 10(1), 840-854. https://doi.org/10.1109/JIOT.2022.3204335.
    12. Seifelnasr, M., AlTawy, R., Youssef, A., & Ghadafi, E. (2023). Privacy-Preserving Mutual Authentication Protocol with Forward Secrecy for IoT–Edge–Cloud. IEEE Internet of Things Journal, 11(5), 8105-8117. https://doi.org/10.1109/JIOT.2023.3318180.
    13. Alruwaili, O., Tanveer, M., Aldossari, S. A., Alanazi, S., & Armghan, A. (2025). RAAF-MEC: Reliable and anonymous authentication framework for IoT-enabled mobile edge computing environment. Internet of Things, 29, 101459. https://doi.org/10.1016/j.iot.2024.101459.
    14. Tanveer, M., & Aldossari, S. A. (2025). RAM-MEN: Robust authentication mechanism for IoT-enabled edge networks. Alexandria Engineering Journal, 112, 436-447. https://doi.org/10.1016/j.aej.2024.10.116.
    15. Zhang, S., & Cao, D. (2024). A blockchain-based provably secure anonymous authentication for edge computing-enabled IoT. The Journal of Su-percomputing, 80(5), 6778-6808. https://doi.org/10.1007/s11227-023-05696-0.
    16. Zhao, G., Chen, H., & Wang, J. (2024). An edge-assisted group authentication scheme for the narrowband internet of things. Complex & Intelligent Systems, 10(5), 6597-6618. https://doi.org/10.1007/s40747-024-01514-z.
    17. Maiti, A., & Kist, A. A. (2025). A Zero-Trust Multi-Processor Reporter-Verifier Design of Edge Devices for Firmware Authenticity in Internet of Things and Blockchain Applications. Journal of Sensor and Actuator Networks, 14(2), 35. https://doi.org/10.3390/jsan14020035.
    18. Zhang, J., Ouda, A., & Abu-Rukba, R. (2024). Authentication and key agreement protocol in hybrid edge–fog–cloud computing enhanced by 5G networks. Future Internet, 16(6), 209. https://doi.org/10.3390/fi16060209.
    19. Shang, W., Wen, X., Chen, Z., Xiong, W., Chang, Z., & Cao, Z. (2024). Lightweight authentication scheme for edge control systems in Industrial Internet of Things. Frontiers of Information Technology & Electronic Engineering, 25(11), 1466-1478. https://doi.org/10.1631/FITEE.2400497.
    20. Al-Shatari, M., Hussin, F. A., Aziz, A. A., Eisa, T. A. E., & Tran, X. T. (2023). IoT Edge Device Security: An Efficient Lightweight Authenticated Encryption Scheme Based on LED and PHOTON. Applied Sciences, 13(18), 10345. https://doi.org/10.3390/app131810345.
    21. Shahidinejad, A., & Abawajy, J. (2023). Decentralized lattice-based device-to-device authentication for the edge-enabled IoT. IEEE Systems Jour-nal, 17(4), 6623-6633. https://doi.org/10.1109/JSYST.2023.3319280.
    22. Cunha, T. B. D., Kiran, M., Ranjan, R., & Vasilakos, A. V. (2024). Physical unclonable functions and QKD-based authentication scheme for IoT devices using blockchain. Internet of Things, 28, 101404. https://doi.org/10.1016/j.iot.2024.101404.
    23. Liu, S., Zhu, L., Zhang, W., Miao, Y., Leng, T., & Choo, K. K. R. (2024). Accuracy-Improved Privacy-Preserving Asynchronous Federated Learn-ing in IoT. IEEE Internet of Things Journal. https://doi.org/10.1016/j.iot.2024.101404.
    24. Zhang, T., Jiang, M., Luo, F., & Guo, Y. (2025). A lattice-based puncturable CP-ABE scheme with forward security for cloud-assisted IoT. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2025.3559567.
    25. Li, G., Lei, H., Liu, F., Li, L., & Jin, H. (2025). AEPPFL: Accurate and efficient privacy protection federal learning in industrial IoT. IEEE Internet of things journal. https://doi.org/10.1109/JIOT.2025.3562064.
    26. Wang, B., Wang, Y., Guo, Q., Lin, Y., & Yu, Y. (2024). Preventive audits for data applications before data sharing in the power IoT. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2024.3483929.
    27. Islam, M., Rehmani, M. H., Gao, L., & Chen, J. (2025). An optimized privacy-utility trade-off framework for differentially private data sharing in blockchain-based internet of things. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2025.3530247.
    28. https://www.unb.ca/cic/datasets/iotdataset-2023.html.

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  • How to Cite

    M. , J., Karuppasamy , K. ., Bhuvaneswari , T. ., Kannan , L. M. ., K. , D. ., & Lakshmi , S. V. . (2025). Temporal Authentication Model for The Internet of Things Edge ‎Devices for Sustainable User Privacy. International Journal of Basic and Applied Sciences, 14(7), 204-212. https://doi.org/10.14419/8jbh3a60