Abstract
To address the issues of static scheduling strategies, coarse-grained privacy protection, and their mutual independence in distributed storage of educational blockchain data, this study hypothesizes that a collaborative mechanism integrating dynamic closed-loop storage scheduling with adaptive graded desensitization can effectively balance storage efficiency and privacy protection strength. To test this hypothesis, this study conducts experiments on the Open University Learning Analytics Dataset (OULAD) educational dataset with six privacy levels and up to 5000 data blocks to evaluate throughput, latency, energy consumption, and classification accuracy. Key findings include: storage throughput reaching 35.6 MB/s with a write latency of 102.9 ms, response time and floating-point operations optimized to 85.6 ms and 33.5 MFLOP, respectively; encryption throughput of 18.7 MB/s with key generation time of only 3.9 ms; and correct classification of 548 out of 600 samples across six privacy levels with energy consumption of 20.5 J. Ablation studies confirm the irreplaceable roles of Coding Storage Allocation and the Hybrid-Dimensional Grid, while sensitivity analysis identifies the optimal configurations for the duplicate block count, learning rate, maximum privacy level, and contraction-expansion coefficient as 2, 0.001, 5, and 1.0 → 0.5, respectively. These results demonstrate that the proposed method effectively resolves the trade-off between storage efficiency and privacy protection for educational blockchain data, providing key technical support for trusted data management in education informatization.
References
S. H. Said, R. S. Sinde, E. M. Kosia, and M. A. Dida, “A comprehensive blockchain-based system for educational qualifications management and verification to counter forgery,” IEEE Access, vol. 13, pp. 31562–31589, February, 2025, DOI: 10.1109/ACCESS.2025.3542545.
P. Gupta and P. Rai, “Can blockchain revolutionize educational practices? An in-depth analysis of applications and challenges,” Sustainable Futures, vol. 10, p. 101171, December, 2025, DOI: 10.1016/j.sftr.2025.101171.
L. Isiaku and A. Adalier, “Analyzing the barriers to blockchain adoption in educational sectors: A comprehensive literature review using the Technology-Organizational-Environment (TOE) framework,” On the Horizon: The International Journal of Learning Futures, vol. 33, no. 1, pp. 32–60, January, 2025, DOI: 10.1108/OTH-05-2024-0029.
T. Wu, G. Jourjon, K. Thilakarathna, and P. L. Yeoh, “MapChain-D: A distributed blockchain for IIoT data storage and communications,” IEEE Trans. Ind. Inform., vol. 19, no. 9, pp. 9766–9776, September, 2023, DOI: 10.1109/TII.2023.3234631.
R. Agrawal, S. Singhal, and A. Sharma, “Blockchain and fog computing model for secure data access control mechanisms for distributed data storage and authentication using hybrid encryption algorithm,” Cluster Comput., vol. 27, no. 6, pp. 8015–8030, April, 2024, DOI: 10.1007/s10586-024-04411-9.
J. Jayabalan and N. Jeyanthi, “Scalable blockchain model using off-chain IPFS storage for healthcare data security and privacy,” J. Parallel Distrib. Comput., vol. 164, pp. 152–167, March, 2022, DOI: 10.1016/j.jpdc.2022.03.009.
J. Li, D. Han, T. H. Weng, H. Wu, K. C. Li, and A. Castiglione, “A secure data storage and sharing scheme for port supply chain based on blockchain and dynamic searchable encryption,” Comput. Stand. Interfaces, vol. 91, p. 103887, January, 2025, DOI: 10.1016/j.csi.2024.103887.
Y. W. Syaifudin, A. P. Prasetya, R. H. Djunaedi, P. Y. Saputra, H. Htet, A. Sunandar, S. Apriliyanto, and V. A. H. Firdaus, “Towards trustless academic records in higher education: integrating blockchain and IPFS for verifiable student credentials,” 2025 Int. Conf. Innov. Intell. Informatics Comput. Technol. (3ICT), Sakhir, Bahrain, 2025, pp. 1–8, DOI: 10.1109/3ict68299.2025.11442139.
W. Liang, Y. Yang, C. Yang, Y. Hu, S. Xie, K. C. Li, and J. Cao, “PDPChain: A consortium blockchain-based privacy protection scheme for personal data,” IEEE Trans. Rel., vol. 72, no. 2, pp. 586–598, January, 2022, DOI: 10.1109/TR.2022.3190932.
A. A. Khan, S. Bourouis, M. M. Kamruzzaman, M. Hadjouni, Z. A. Shaikh, A. A. Laghari, Hela elma Elmannai, and S. Dhahbi, “Data security in healthcare industrial internet of things with blockchain,” IEEE Sens. J., vol. 23, no. 20, pp. 25144–25151, October, 2023, DOI: 10.1109/JSEN.2023.3273851.
X. Li, H. Zhao, and W. Deng, “BFOD: Blockchain-based privacy protection and security sharing scheme of flight operation data,” IEEE Internet Things J., vol. 11, no. 2, pp. 3392–3401, January, 2023, DOI: 10.1109/JIOT.2023.3296460.
A. Hussain, L. Javed, M. I. U. Haq, R. Khan, W. Akbar, R. W. Attar, A. Alhazmi, A. H. Alhazmi, and T. Hussain, “SecureChain: A blockchain-based secure model for sharing privacy-preserved data using local differential privacy,” Concurrency Comput. Pract. Exp., vol. 38, no. 4, p. e70473, January, 2026, DOI: 10.1002/cpe.70473.
B. Wang, P. Yang, R. Meng, X. Yang, and J. Wu, “PCTRS: Enhancing privacy and control in electronic medical records through blockchain and threshold traceable ring signatures,” IEEE Internet Things J., vol. 13, no. 6, pp. 11493–11506, March, 2026, DOI: 10.1109/JIOT.2025.3646619.
Y. Lu and S. A. Jafar, “Quantum X-Secure T-Private information retrieval from MDS Coded storage with unresponsive and Byzantine servers,” IEEE J. Sel. Areas Inf. Theory, vol. 6, no. 1, pp. 59–73, March, 2025, DOI: 10.1109/JSAIT.2025.3554625.
Y. Luo, Y. Chen, Z. Ouyang, W. Tan, and X. Yang, “Privacy-enhanced high-fidelity separable lossless reversible data hiding,” IEEE Trans. Inf. Forensics Security, vol. 20, no. 1, pp. 9180–9194, August, 2025, DOI: 10.1109/TIFS.2025.3602215.
J. Haghighat and T. M. Duman, “Analysis of coded slotted ALOHA with energy harvesting nodes for perfect and imperfect packet recovery scenarios,” IEEE Trans. Wireless Commun., vol. 22, no. 11, pp. 7424–7437, November, 2023, DOI: 10.1109/TWC.2023.3250550.
X. Li, H. Yan, Z. Cheng, W. Sun, and H. Li, “Protecting regression models with personalized local differential privacy,” IEEE Trans. Dependable Secure Comput., vol. 20, no. 2, pp. 960–974, January, 2022, DOI: 10.1109/TDSC.2022.3144690.
J. Wang, J. Chen, N. Xiong, O. Alfarraj, A. Tolba, and Y. Ren, “S-BDS: An effective blockchain-based data storage scheme in zero-trust IoT,” ACM Trans. Internet Technol., vol. 23, no. 3, pp. 1–23, February, 2023, DOI: 10.1145/3511902.
H. Wang, G. Liu, and H. Shen, “Cooperative job scheduling and data allocation in data-intensive parallel computing clusters,” IEEE Trans. Cloud Comput., vol. 11, no. 3, pp. 2392–2406, January, 2022, DOI: 10.1109/TCC.2022.3206206.
R. Wertenbroek, Y. Thoma, and A. Dassatti, “A portable Linux-based firmware for NVMe computational storage devices,” ACM Trans. Storage, vol. 21, no. 2, pp. 1–36, September, 2025, DOI: 10.1145/3697352.
V. V. Vegesna, “Methodologies for enhanced data integrity and security in distributed cloud computing with techniques to implement security solutions,” Asian J. Appl. Sci. Technol., vol. 6, no. 1, pp. 167–180, January, 2022, DOI: 10.38177/ajast.2022.6217.
I. Bouleghlimat, S. Boudouda, and S. Hacini, “PPSecS: privacy-preserving secure big data storage in a cloud environment,” Arab. J. Sci. Eng., vol. 49, no. 3, pp. 3225–3239, May, 2024, DOI: 10.1007/s13369-023-07924-4.
M. Zare, Y. E. Sola, and H. Hasanpour, “Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm,” J. King Saud Univ. Comput. Inf. Sci., vol. 35, no. 1, pp. 368–381, December, 2023, DOI: 10.1016/j.jksuci.2022.12.006.
K. Moghaddasi, S. Rajabi, and F. S. Gharehchopogh, “An enhanced asynchronous advantage actor-critic-based algorithm for performance optimization in mobile edge computing-enabled internet of vehicles networks,” Peer-to-Peer Netw. Appl., vol. 17, no. 3, pp. 1169–1189, February, 2024, DOI: 10.1007/s12083-024-01633-x.
D. Zhang, Z. Ye, D. Zhang, and Q. Lu, “A dynamic event-triggered network control algorithm combined with gradient-sharing asynchronous advantage actor-critic strategy,” Trans. Inst. Meas. Control, vol. 47, no. 13, pp. 2767–2780, March, 2025, DOI: 10.1177/01423312231159698.
W. Wang, Y. Wang, P. Duan, T. Liu, X. Tong, and Z. Cai, “A triple real-time trajectory privacy protection mechanism based on edge computing and blockchain in mobile crowdsourcing,” IEEE Trans. Mobile Comput., vol. 22, no. 10, pp. 5625–5642, January, 2022, DOI: 10.1109/TMC.2022.3187047.
M. Kataev and L. Bulysheva, “Blockchain system in the higher education: Storing academical students’ records and achievements accumulated in the educational process,” Syst. Res. Behav. Sci., vol. 39, no. 3, pp. 589–596, June, 2022, DOI: 10.1002/sres.2872.
L. Holly, S. Thom, M. Elzemety, B. Murage, K. Mathieson, and M. I. Iñigo Petralanda, “Strengthening health data governance: new equity and rights-based principles,” Int. J. Health Gov., vol. 28, no. 3, pp. 225–237, March, 2023, DOI: 10.1108/IJHG-11-2022-0104.
Y. Ni, J. Li, W. Chang, and J. Xiao, “An LDP-based privacy-preserving longitudinal and multidimensional range query scheme in IoT,” IEEE Internet Things J., vol. 11, no. 3, pp. 5210–5221, January, 2023, DOI: 10.1109/JIOT.2023.3306003.
Q. Liu, Z. Wang, X. Zhou, Y. Zhang, X. Liu, and H. Lin, “GSFL: A privacy-preserving grouping-split federated learning approach in resource-constrained edge computing scenarios,” ACM Trans. Auton. Adapt. Syst., vol. 20, no. 4, pp. 1–29, March, 2025, DOI: 10.1145/3725221.
E. Tire and M. E. Gursoy, “Answering spatial density queries under local differential privacy,” IEEE Internet Things J., vol. 11, no. 10, pp. 17419–17436, May, 2024, DOI: 10.1109/JIOT.2024.3357570.
E. Baş, “Improved particle swarm optimization on based quantum behaved framework for big data optimization,” Neural Process. Lett., vol. 55, no. 3, pp. 2551–2586, May, 2023, DOI: 10.1007/s11063-022-10850-5.
C. E. Garcia, M. R. Camana, and I. Koo, “Ensemble learning aided QPSO-based framework for secrecy energy efficiency in FD CR-NOMA systems,” IEEE Trans. Green Commun. Netw., vol. 7, no. 2, pp. 649–667, November, 2022, DOI: 10.1109/TGCN.2022.3219111.
M. Chen, Y. Zhao, Y. Li, P. Peng, and X. Tang, “Optimal operation of Internet Data Center with PV and energy storage type of UPS clusters,” Glob. Energy Interconnect., vol. 7, no. 1, pp. 61–70, February, 2024, DOI: 10.1016/j.gloei.2024.01.006.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Journal of Cyber Security and Mobility