A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services. Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices. However, when users utilize these services, they inevitably expose personal information such as their ID and sensitive location to the servers. Due to untrustworthy servers and malicious attackers with colossal background knowledge, users' personal information is at risk on these servers. Unfortunately, many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment. We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users. Firstly, Staying Points Detection Method based on Temporal-Spatial Restrictions (SPDM-TSR) is an interest area mining method based on temporal-spatial restrictions, which can clearly distinguish between staying and moving points. Additionally, our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory. Furthermore, our proposed mechanism does not rely on third-party service providers and the attackers' background knowledge settings. We test our models on real datasets, and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.