数据与计算发展前沿2026，Vol.8Issue(2)：227-240,14.DOI:10.11871/jfdc.issn.2096-742X.2026.02.017
基于敏感性分级的教育数据可信存储模型研究
Research on the Trusted Storage Model of Educational Data Based on Sensitivity Grading
摘要
Abstract
[Objective]Addressing the current issue of a lack of unified classification and grading stan-dards for educational data,which leads to difficulties in data security governance due to the ab-sence of standardized classification and grading management within the industry,this paper es-tablishes rules for the classification and grading of educational data and proposes a trusted stor-age 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-chan-nel hierarchical storage structure was constructed to achieve isolated storage of sensitive data,effectively safe-guarding 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 se-cure data storage.Compared to traditional blockchain storage architectures,this solution reduces storage over-head 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.关键词
教育数据分类分级/敏感性分级/HDFS/区块链/可信存储Key words
education data classification and grading/sensitivity grading/HDFS/blockchain/trusted storage引用本文复制引用
赵若含,袁凌云..基于敏感性分级的教育数据可信存储模型研究[J].数据与计算发展前沿,2026,8(2):227-240,14.基金项目
国家自然科学基金资助项目(62262073) （62262073）
云南省应用基础研究计划资助项目(202101AT070098) （202101AT070098）
云南省万人计划青年拔尖人才资助项目(YNWR-QNBJ-2019-237) （YNWR-QNBJ-2019-237）
云南省重大科技专项计划(20240-2AD080002) （20240-2AD080002）
