数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (2): 227-240.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.02.017

doi: 10.11871/jfdc.issn.2096-742X.2026.02.017

• 技术与应用 • 上一篇    下一篇

基于敏感性分级的教育数据可信存储模型研究

赵若含1(),袁凌云1,2,*()   

  1. 1 民族教育信息化教育部重点实验室（云南师范大学）， 云南 昆明 650500
    2 云南师范大学， 信息学院， 云南 昆明 650500
  • 收稿日期:2025-05-31 出版日期:2026-04-20 发布日期:2026-04-23
  • 通讯作者: *袁凌云（E-mail: blues520@sina.com）
  • 作者简介:赵若含，云南师范大学，硕士研究生，主要研究方向为区块链、教育大数据安全治理。
    本文主要承担的工作为论文撰写、模型设计以及模型性能测试。
    ZHAO Ruohan is a master's student at Yunnan Normal University. Her research interests include blockchain and education big data security governance.
    In this paper, she is mainly responsible for paper writing, model design, and model performance testing.
    E-mail：2368730726@qq.com|袁凌云，博士，云南师范大学教授，主要研究方向为物联网安全、区块链、传感器网络。近年来承担国家自然科学基金项目1项、国家863 计划项目子课题1项、国家教育部人文社科青年基金项目1项、云南省应用基础研究计划项目2项、云南省教育厅项目1项，参与国家级省部级项目14项。
    在本文中负责论文修改和指导相关工作。
    YUAN Lingyun, Ph.D., is a professor at Yunnan Normal University. Her research interests include IoT security, blockchain, and sensor networks. In recent years, she has led one project funded by the National Natural Science Foundation of China, one sub-project of the National High-Tech Research and Development Program (863 Program), one project funded by the Ministry of Education's Humanities and Social Sciences Youth Fund, two projects funded by Yunnan Province's Applied Basic Research Program, and one project funded by Yunnan Provincial Department of Education. Additionally, she has participated in 14 national, provincial, and ministerial-level projects.
    In this paper, she is mainly responsible for revising the paper and providing guidance.
    E-mail：blues520@sina.com
  • 基金资助:
    国家自然科学基金资助项目(62262073);云南省应用基础研究计划资助项目(202101AT070098);云南省万人计划青年拔尖人才资助项目(YNWR-QNBJ-2019-237);云南省重大科技专项计划(20240-2AD080002)

Research on the Trusted Storage Model of EducationalData Based on Sensitivity Grading

ZHAO Ruohan1(),YUAN Lingyun1,2,*()   

  1. 1 Key Laboratory of Ethnic Educational Information, Ministry of Education, Yunnan Normal University, Kunming, Yunnan 650500, China
    2 College of Information Science & Technology, Yunnan Normal University, Kunming, Yunnan 650500, China
  • Received:2025-05-31 Online:2026-04-20 Published:2026-04-23

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摘要：

【目的】 针对当前教育数据尚未形成统一的分类分级标准，行业内缺乏数据分类分级管理造成教育数据安全治理难的问题，本文创建了教育数据分类分级规则，并提出了一种基于敏感性分级的教育数据可信存储模型。【方法】 首先，通过“区块链+HDFS”链上链下协同存储的方式，缓解区块链存储瓶颈的同时保证数据存储的安全和效率；其次，构建多通道分级存储结构，实现敏感数据的隔离存储，有效保障了敏感数据的安全性和可信度；最后，通过部署智能合约实现数据的自动化和差异化存储管理，为敏感数据提供安全等级更高的防护管理措施。【结果】 实验结果表明，该模型在保证数据安全存储的前提下具备较好的存储效率。与传统的区块链存储架构相比，该方案的存储开销下降31%，时间开销减少98%，资源开销也有明显降低。【结论】 该模型能满足大规模教育数据的存储需求，增强了教育敏感数据的隐私保护。

关键词: 教育数据分类分级, 敏感性分级, HDFS, 区块链, 可信存储

Abstract:

[Objective] Addressing the current issue of a lack of unified classification and grading standards for educational data, which leads to difficulties in data security governance due to the absence of standardized classification and grading management within the industry, this paper establishes rules for the classification and grading of educational data and proposes a trusted storage model for educational data based on sensitivity grading. [Methods] Firstly, the “blockchain +HDFS” collaborative on-chain and off-chain storage approach was employed to alleviate blockchain storage bottlenecks while ensuring the security and efficiency of data storage. Secondly, a multi-channel hierarchical storage structure was constructed to achieve isolated storage of sensitive data, effectively safeguarding the security and credibility of such data. Finally, the deployment of smart contracts enabled automated and differentiated storage management of data, providing higher-level protection measures for sensitive data. [Results] Experimental results demonstrate that this model achieves good storage efficiency while ensuring secure data storage. Compared to traditional blockchain storage architectures, this solution reduces storage overhead by 31%, reduces time overhead by 98%, and also significantly reduces resource overhead. [Conclusions] It meets the storage requirements for large-scale educational data and enhances the privacy protection of sensitive educational data.

Key words: education data classification and grading, sensitivity grading, HDFS, blockchain, trusted storage