This research paper presents a privacy-preserving digital contact tracing system using Bluetooth Low Energy (BLE) designed to detect close contact interactions while protecting user privacy. Traditional contact tracing systems often rely on centralized databases and location tracking technologies such as GPS, which may expose sensitive personal data. To overcome these limitations, the proposed system adopts a decentralized architecture where mobile devices exchange temporary anonymous identifiers using BLE. The system periodically generates rotating identifiers that are stored locally on user devices. When two users come into close proximity, their devices exchange these identifiers and record the interaction without revealing personal identities or location information. If a user reports an infection, the system allows voluntary upload of anonymous identifiers so that other devices can locally check for possible exposure. Experimental evaluation demonstrates that BLE-based proximity detection provides high accuracy, improved energy efficiency, and stronger privacy protection compared to GPS-based approaches. Performance metrics such as accuracy, precision, recall, and F1-score confirm the effectiveness of the proposed system in identifying exposure events while minimizing false alerts. The results indicate that the proposed BLE-based contact tracing framework provides a scalable, secure, and privacy-aware solution for epidemic monitoring, making it suitable for real-world deployment during public health emergencies.
Mote et al. (Tue,) studied this question.