Comprehensive location privacy enhanced model.

With the increasing popularity of location-based services (LBSs), safeguarding location privacy has become critically important. Traditional methods often struggle to balance the intensity of privacy protection with service quality. To address this challenge, this research proposes the comprehensive location privacy enhanced model (CLPEM), which enhances personalized privacy protection by integrating dynamic weight allocation at the policy layer, incorporating a user feedback mechanism, and designing tailored privacy strategies for various scenarios. Additionally, the model employs data fusion and optimization techniques to enhance the usability of location data while ensuring effective privacy protection. Our experimental results demonstrate that CLPEM outperforms existing technologies in terms of privacy strength, data availability, and user satisfaction, providing a robust technical framework for location privacy and paving the way for future research and applications.