In today’s world, where wireless connectivity is intertwined with everyday life, privacy is not just a concept - it is a fundamental right. Proximity detection technologies have evolved significantly in recent years, particularly during the COVID-19 pandemic, when digital contact tracing was implemented by many countries to combat the virus’s spread. Bluetooth Low Energy (BLE) technology has been a natural choice for digital contact tracing due to its efficiency, low power consumption, and widespread adoption in mobile phones.
Viktoriia Shubina’s doctoral dissertation delves into the impact BLE Received Signal Strength (RSS) on user privacy in proximity detection applications. She evaluates wireless positioning algorithms, highlighting their strengths and limitations. Shubina introduces performance metrics for comparing factors like accuracy and utility that could affect privacy. She presents a BLE-based approach to address privacy concerns in digital contact tracing or social-network applications. Her work underscores the importance of privacy trade-offs in BLE proximity detection.
“BLE RSS is currently utilised by consumers in various applications, such as proximity detection or location-based notifications. These everyday uses demonstrate the growing importance of BLE in enhancing user experiences while maintaining privacy,” explains Shubina.
Her study includes measurements, simulation, and theoretical modelling. Measurement campaigns conducted at Tampere University (TAU) and University 'Politehnica' of Bucharest (UPB) in collaboration of research efforts, reveal various sources of BLE errors. These include hardware-related variations in RSS, the impact of the BLE advertising channel index on positioning accuracy, environmental factors, hardware orientation, and interactions with 2.4 GHz band devices like Wi-Fi.
While maintaining the accuracy of proximity detection, Shubina identifies potential privacy risks and provides strategies to mitigate them. By implementing privacy-preserving techniques, including added noise, obfuscation, and a novel argmax-based mechanism, she manages to enhance privacy protection while maintaining detection probabilities at 90% and false-alarm probabilities below 15%.
In summary, this doctoral dissertation introduces a privacy-aware algorithm that is applicable in digital contact-tracing scenarios and conducts empirical and theoretical studies to evaluate privacy-utility/accuracy trade-offs.
Viktoriia Shubina conducted her research between 2019–2023 under A-WEAR project (A network for dynamic wearable applications with privacy constraints), a Marie Sklodowska-Curie European Joint Doctorate/Innovative Training Network, at Tampere University, Finland and University Politehnica of Bucharest, Romania. A-WEAR received funding through the Horizon 2020 Research and Innovation Programme under the Grant Agreement no. 813278.
Public defence on Wednesday 13 December
The doctoral dissertation of MSc. Viktoriia Shubina in the field of wireless technologies titled Privacy-aware BLE Proximity Detection through Optimizing Trade-offs will be publicly examined at the Faculty of Information Technology and Communication Sciences of Tampere University in room TB109 in the Tietotalo building (address: Korkeakoulunkatu 1, Tampere) at 12:00 on Wednesday 13 December 2023.
The opponent will be Associate Professor Andrei Arusoaie from Alexandru Ioan Cuza University of Iasi, Romania. The Custos will be Professor Simona Lohan from Tampere University. The work has been co-supervised by Professor Dragoș Niculescu from University ‘Politehnica’ of Bucharest and Dr. Aleksandr Ometov from Tampere University.
Photo: Henrik Schmuul