Key Method for Privacy Protection of Trajectory Data

With the extensive proliferation of location-based services, protecting user trajectory privacy against continuous query attacks has become a critical challenge. Existing protection mechanisms often suffer from a rigid trade-off between privacy strength and service quality. To bridge this gap, this study proposes a unified demand-aware trajectory privacy protection framework. First, a fake trajectory generation algorithm is developed that ensures to resist advanced inference attacks. Second, a maximizing demand request algorithm is introduced to resolve conflicts between privacy demands and sparse historical data. Finally, two anonymous zone minimization strategies are implemented. Experimental results using real-world mobility generators demonstrate that the proposed framework improves the anonymous service success rate by more than 13% over baseline location privacy-preserving algorithms while maintaining a smaller anonymous area, balancing privacy and utility