Improving Privacy in Educational Data: Developing an Anonymization Algorithm Using AES/GCM and Mixing Methods

Objective: to analyze existing methods and methods of depersonalization of students’ personal data, as well as to develop algorithms for their depersonalization to ensure information security in educational institutions, taking into account the requirements of Russian legislation. Methods: comparison of existing approaches for depersonalization of personal data. A hybrid algorithm for depersonalizing personal data is proposed, which combines the method of changing the composition or semantics and the mixing method based on modern cryptographic standards. A comprehensive algorithm for depersonalization of personal data has been developed. Results: enabling educational organizations to legally transfer information for statistical analysis, validation of methodologies, software testing, or applied research purposes without violating the rights of data subjects, while reducing the risks of unauthorized access. Practical significance: a comprehensive algorithm for de-identification of personal data intended for use in the infrastructure of educational institutions has been created and experimentally substantiated. This algorithm, based on a combination of semantic structure transformation, mixing of records and the use of up-to-date cryptographic protocols and standards, provides the possibility of legitimate processing of students’ personal data in compliance with regulatory requirements that are enshrined in Russian legislation on information, information security and personal data.