Location privacy protection method based on social network platform

Abstract

In recent years, rapid advancements in wireless communication and positioning technologies have made location-based services (LBS) common and highly convenient in daily life, from navigation to social networking applications. However, this convenience often comes at the expense of user privacy, raising significant security concerns regarding unauthorized access and misuse of location data. This research addresses the dual nature of LBS by highlighting the critical need for robust and practical privacy mechanisms to safeguard sensitive geolocation data. Specifically, this paper proposes a novel privacy-preserving method leveraging Application Programming Interface (API) hijacking technology integrated into social network platforms. Through intercepting and perturbing location-based API calls, the method enhances privacy protection with minimal disruption to the user experience. Simulation experiments utilizing over 10,000 real-world QQ check-in records demonstrate that injecting random noise (ranging from 0.0001°–0.01°, approximately 11 m–1.1 km) significantly increases median location error from approximately 11 m to over 1 km, while introducing negligible latency overhead of only 15±3 milliseconds. This favorable trade-off confirms the method’s practical effectiveness in achieving a balance between privacy enhancement and service utility. Furthermore, this study critically reviews existing location privacy solutions, identifies their limitations, and introduces API hijacking as an innovative perspective for location privacy protection on popular social media platforms.