Enhancing student data privacy in virtual learning with blockchain and advanced encryption - Yuanyuan Fan (PLoS ONE, 2026)
This study investigates a novel approach to enhance student data privacy in virtual collaborative learning environments (VCLEs). With the increasing adoption of VCLEs, students are generating substantial personal information within these digital spaces, including identification details, academic records, and learning preferences. The compromise or misuse of such data can infringe on students’ rights and diminish their learning motivation. Existing privacy paradigms often have limitations in addressing these challenges, such as the risk of leakage of personally identifiable information, data storage vulnerabilities, and inflexibility in access management. This research proposes a new method that leverages Localized Differential Privacy (LDP) and Attribute-Based Searchable Encryption (ABSE) within a blockchain framework to address these privacy issues. The LDP technique, specifically the Randomized Aggregable Privacy-Preserving Ordinal Response (RAPPOR), is employed for data pre-processing to conceal student identities. Subsequently, a combination of Searchable Encryption (SE) and Attribute-Based Encryption (ABE) ensures controlled data access while safeguarding information privacy. The proposed framework integrates blockchain technology with a cloud server for secure data storage and keyword-based indexing. Evaluations demonstrate the superiority of the proposed model over traditional methods, with improvements in accuracy, efficiency, and security. Furthermore, its implementation in a VCLE setting validates its practical applicability, addressing key privacy issues faced by students. This research advances the field of educational data privacy by presenting a pioneering solution tailored for VCLEs.