Privacy-preserving proximity-based attendance verification using passive mobile device signals

Abstract

The increasing adoption of smart technologies in educational institutions has accelerated the transition from man ual attendance recording to automated attendance management systems. However, existing approaches such as biometric authentication, Radio Frequency Identification (RFID) cards, and mobile-based check-ins often present significant challenges related to privacy, infrastructure costs, and vulnerability to proxy attendance. This study pro poses a privacy-preserving proximity-based attendance verification system that utilizes passive wireless signals emitted by mobile devices, specifically, Bluetooth Low Energy (BLE) advertisements and Wi-Fi probe requests, to infer physical presence without collecting personally identifiable information. The proposed framework integrates Received Signal Strength Indicator (RSSI)-based proximity estimation, temporal aggregation and signal filtering techniques filtering techniques to support reliable attendance verification in classroom environments. To address privacy concerns, the framework adopts privacy-by-design principles, including real-time processing, avoidance of persistent device identifiers, anonymised signal handling, and minimal data retention. The proposed architecture consists of signal acquisition, processing and inference, and attendance decision layers that collectively enable pas sive and non-intrusive attendance monitoring. A comparative analysis with existing attendance systems highlights the potential advantages of the frameworks in terms of privacy preservation, scalability, cost-effectiveness, and re sistance to attendance fraud. Although the framework has not yet undergone full-scale experimental validation, it provides a foundation for future prototype development and empirical evaluation. The study contributes to ongoing research in privacy-aware Internet of Things (IoT) systems and smart educational technologies by presenting a de sign-oriented approach to attendance verification that balances operational efficiency with user privacy.