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Read moreThe Quantum-Blockchain Fusion Framework (QBFF) introduces a pioneering solution for dynamic access control in federated grid computing systems. Addressing critical challenges in security, scalability, and adaptability, QBFF combines quantum key distribution (QKD) for secure communication with blockchain technology for decentralized identity management and policy enforcement. The framework employs a hybrid consensus mechanism, integrating proof-of-authority and quantum randomness to enhance efficiency while maintaining robust security. Experimental results demonstrate significant improvements in transaction throughput, reduced latency, and resilience against cyber threats like identity spoofing and consensus manipulation. QBFF also supports dynamic policy updates with real-time adaptability, enabling scalable and efficient operations in distributed environments. This research establishes QBFF as a comprehensive solution for modern grid systems, ensuring security and scalability in the face of evolving technological demands.
References
1.Raina, P., & Shah, H. Data-Intensive Computing on Grid Computing Environment. International Journal of Open Publication and Exploration (IJOPE), ISSN, 3006-2853.
2.Chen, T., & Liu, C. (2024). Soft computing-based smart grid fault detection uses computerised data analysis with a fuzzy machine learning model. Sustainable Computing: Informatics and Systems, 41, 100945.
3.Sarker, M. A. A., Shanmugam, B., Azam, S., & Thennadil, S. (2024). Enhancing intelligent grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability. Intelligent Systems with Applications, 23, 200422.
4.Shang, Y., Li, Z., Li, S., Shao, Z., & Jian, L. (2024). An Information Security Solution for Vehicleto-grid Scheduling by Distributed Edge Computing and Federated Deep Learning. IEEE Transactions on Industry Applications.
5.Qiao, C., Li, M., Liu, Y., & Tian, Z. (2024). Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey. IEEE Communications Surveys & Tutorials.
6.Liu, J., Chen, C., Li, Y., Sun, L., Song, Y., Zhou, J., ... & Dou, D. (2024). Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning. Knowledge and Information Systems, 1-27.
7.Putra, M. A. P., Alief, R. N., Rachmawati, S. M., Sampedro, G. A., Kim, D. S., & Lee, J. M. (2024). Proof-of-authority-based secure and efficient aggregation with differential privacy for federated learning in industrial IoT. Internet of Things, 25, 101107.
8.Akanfe, O., Lawong, D., & Rao, H. R. (2024). Blockchain technology and privacy regulation: Reviewing frictions and synthesizing opportunities. International Journal of Information Management, 76, 102753.
9.Deng, H., Liang, J., Zhang, C., Liu, X., Zhu, L., & Guo, S. (2024). FutureDID: A Fully Decentralized Identity System with Multi-Party Verification. IEEE Transactions on Computers.
10. Deep, A., Perrusquía, A., Aljaburi, L., Al-Rubaye, S., & Guo, W. (2024). A Novel Distributed Authentication of Blockchain Technology Integration in IoT Services. IEEE Access.
11. Liu, Y., He, J., Li, X., Chen, J., Liu, X., Peng, S., ... & Wang, Y. (2024). An overview of blockchain ingenious contract execution mechanism. Journal of Industrial Information Integration,
12. Liu, A., Chen, J., He, K., Du, R., Xu, J., Wu, C., ... & Ma, J. (2024). DYNASHARD: Secure and Adaptive Blockchain Sharding Protocol with Hybrid Consensus and Dynamic Shard Management. IEEE Internet of Things Journal.
13. Sheikh, S., Shahid, M., Sambare, M., Haidri, R. A., & Prakash, S. (2024). A Load Distribution Based Resource Allocation Strategy for Bag of Tasks (BoT) in Computational Grid Environment. Wireless Personal Communications, 1-34.
14. Τσίλης, Χ. (2024). Evaluation and characteristics of the development standards for decentralized identities (DIDs).
15. Wang, Z., Goudarzi, M., Gong, M., & Buyya, R. (2024). Deep reinforcement learning-based scheduling optimises system load and response time in edge and fog computing environments. Future Generation Computer Systems, 152, 55-69.
17.Liu, P., Wang, J., Ma, K., & Guo, Q. (2024). Joint Cooperative Computation and Communication for Demand-Side NOMA-MEC Systems With Relay-Assisted in Smart Grid Communications. IEEE Internet of Things Journal.
18.Zhang, C., Lu, P., Qiu, W., Kuang, X., Tu, R., & Zhang, S. (2024). High-throughput thermodynamic analysis of epitaxial growth of β-Ga2O3 by the chemical vapour deposition method from TMGa-H2O system. Materials Today Communications, 38, 108054.
19.Fiorini, F., Pagano, M., Garroppo, R. G., & Osele, A. (2024). Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator. Future Internet, 16(8), 275.
20.Zheng, L., Cui, Y., Jin, S., & Chen, Y. (2024). High-Performance Computing-based Open-Source Power Transmission and Distribution Grid Co-Simulation. IEEE Transactions on Power Systems.
Quantum-Blockchain Fusion Framework; Quantum Key Distribution;Federated Grid Computing; Decentralized Identity Management; DynamicAccess Control; Hybrid Consensus Mechanism.
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