A Secure and Privacy-Preserving Architecture for Web-Based Remote Learning Systems

Abstract

The rapid proliferation of web-based remote learning systems (WBRL) has introduced critical security and privacy challenges that threaten the integrity of educational data and the confidentiality of student information. This paper presents SP-WBRL, a Secure and Privacy-Preserving architecture designed for web-based remote learning environments. The proposed multi-layered framework integrates advanced encryption standards (AES-256), role-based access control (RBAC), differential privacy mechanisms, and a zero-trust network model to provide end-to-end security. We formalize the privacy guarantees using (ε, δ)-differential privacy and derive theoretical bounds on information leakage. Extensive experiments conducted over a 12-week deployment with 2,847 participants across three institutions demonstrate that SP-WBRL achieves a 97.3% threat detection rate with only 6.8% average latency overhead compared to non-secure baselines, while maintaining FERPA and GDPR compliance. Comparative evaluation against five state-of-the-art systems shows significant improvements in security coverage (23.1% improvement), privacy preservation (31.5% improvement), and user satisfaction (92.4% approval rating). The results confirm that comprehensive security can be integrated into remote learning platforms without substantially degrading the user experience.