Location Privacy Protection through Road Network Adaptability and User Mobility Prediction
The widespread collection and sharing of location data enable a wide range of location-based services but also raise significant privacy concerns, as mobility traces can reveal highly sensitive personal information. Geo-Indistinguishability has emerged as a principled approach to location privacy by adding controlled noise to users’ positions. However, existing mechanisms typically rely on fixed privacy budgets or adapt them based solely on past or current locations, while often ignoring both future mobility patterns and road network constraints. In this paper, we propose location privacy-preserving mechanisms that leverage the structure of road networks, as well as mobility prediction, to improve the achieved privacy-utility trade-off. To do so, we developed two novel complementary approaches that: (i) adapt the privacy budget dynamically based on the prediction of future locations, and (ii) aggregate locations according to the proximity of their predicted future positions. Experimental results show that incorporating predictability of upcoming locations enables more effective privacy budget allocation, improves utility, and increases resilience against location prediction attacks. These findings highlight prediction-aware obfuscation as a promising direction for enhancing Geo-Indistinguishability-based location privacy mechanisms.