Frontiers of Data and Computing ›› 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

• Technology and Application • Previous Articles     Next Articles

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

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