Research on Classification and Desensitization Strategies of Sensitive Educational Data

² School of Smart Education, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China; Jiangsu Education Informatization Engineering Technology Research Center, Xuzhou 221116, Jiangsu, China,

JCER 2025 , 9(4), 141–146; https://doi.org/10.26689/jcer.v9i4.10263

© 2025 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )

Download PDF

Cite

XML

HTML

Abstract

In the era of digital intelligence, data is a key element in promoting social and economic development. Educational data, as a vital component of data, not only supports teaching and learning but also contains much sensitive information. How to effectively categorize and protect sensitive data has become an urgent issue in educational data security. This paper systematically researches and constructs a multi-dimensional classification framework for sensitive educational data, and discusses its security protection strategy from the aspects of identification and desensitization, aiming to provide new ideas for the security management of sensitive educational data and to help the construction of an educational data security ecosystem in the era of digital intelligence.

References

Ministry of Education of the People’s Republic of China, 2024, Ministry of Education Convenes 2024 National Digitization of Education Work Summary Conference, viewed January 20, 2025, http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/moe_1485/202412/t20241227_1171791.html

Lu C, 2021, Research on Data Desensitization Technology for Internet of Things, Master’s thesis, Nanjing University of Posts and Telecommunications. https://doi.org/10.27251/d.cnki.gnjdc.2021.000420

The Central People’s Government of the People’s Republic of China, 2021, Data Security Law of the People’s Republic of China, viewed December 24, 2024, https://www.gov.cn/xinwen/2021-06/11/content_5616919.htm

Yang X, Tang S, Li J, 2016, The Definition, Potential Value and Challenges of Big Data in Education. Modern Distance Education Research, 1: 50–61.

Wu J, Su Y, Jiang Z, et al., 2022, Analysis of Sensitive Data Storage Security Technology in Power Monitoring Systems. Electronic Technology and Software Engineering, (05): 188–191. https://doi.org/10.20109/j.cnki.etse.2022.05.045

Yuan M, Xu X, Zhang Y, 2023, Research on AI-Based Method for Data Masking of Unstructured Operational Data. Cybersecurity and Data Governance, 42(S1): 184–190.

An M, 2022, Research and Implementation of a Sensitive Content Recognition Mechanism for Big Data Security, Master’s thesis, Beijing Jiaotong University.

Shen C, Xu Y, 2023, Research and Prospect of Data Masking Technology. Information Security and Communications Confidentiality, (02): 105–116.

Cai B, 2024, Research on Multi-Dimensional Data De-Identification Method Based on Differential Privacy. Information Recording Materials, 25(05): 160–162.

McMahan B, Moore E, Ramage D, et al., 2017, Communication-Efficient Learning of Deep Networks from Decentralized Data, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54: 1273–1282.

Previous article in this issue

Next article in this issue