A Framework for Protecting Students Privacy in Online and Offline Digital Footprints

Olebara C; Uchendu Clement

doi:10.56201/ijcsmt.vol.12.no7.2026.pg51.68

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A Framework for Protecting Students Privacy in Online and Offline Digital Footprints

Olebara C, Uchendu Clement

Abstract

The increasing adoption of digital learning technologies within higher education institutions has significantly expanded the generation of students’ online and offline digital footprints. Learning Management Systems , biometric attendance systems, smart campus infrastructures, and institutional databases continuously collect and process sensitive student data across heterogeneous environments. However, existing educational systems often manage these datasets independently, resulting in fragmented privacy controls, unauthorized data correlation, identity exposure, and heightened risks of re-identification. This study proposes a Privacy-by-Design -enabled hybrid privacy framework for protecting students’ digital footprints in modern educational environments. The study adopted a Design Science Research methodology together with experimental simulation for the development and evaluation of the proposed framework. The framework integrates multiple privacy-preserving mechanisms, including salted SHA-256 hashing for secure identity synchronization, k-anonymity (k = 5) for anonymization, differential privacy for inference attack resistance, and Attribute-Based Access Control under a Zero Trust Architecture for secure access governance. A student privacy dashboard was further incorporated to enhance transparency, consent management, and user control over personal data. To evaluate the effectiveness of the framework, a synthetic dataset containing 50,000 student records representing both online and offline educational interactions was generated and analyzed. Experimental results demonstrated substantial improvements in privacy protection, reduced identity disclosure risks, improved transparency, and enhanced secure data management while maintaining acceptable system scalability and analytical utility. The study contributes a scalable and integrated privacy-preserving framework capable of addressing the growing challenges associated with hybrid student digital footprints in contemporary educational systems.

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A Framework for Protecting Students Privacy in Online and Offline Digital Footprints

Abstract

References

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