Spatiotemporal Mobility Based Trajectory Privacy-Preserving Algorithm in Location-Based Services.

Area of Science:

• Computer Science
• Information Security
• Data Privacy

Background:

• Location-Based Services (LBSs) are widely used, but raise concerns about user trajectory privacy.
• Existing trajectory k-anonymity methods often neglect user attributes, leaving trajectories vulnerable.
• There is an urgent need to enhance privacy protection in LBSs without compromising service quality.

Purpose of the Study:

• To develop a novel algorithm for preserving trajectory privacy in LBSs.
• To address the vulnerability of existing k-anonymity techniques by incorporating user attributes.
• To improve the effectiveness of privacy protection while maintaining high service quality.

Main Methods:

• Definition of a Spatiotemporal Mobility (SM) measurement to quantify attribute-set relationships.
• Design of a trajectory graph to model inter-trajectory relationships.
• Development of the SM-based trajectory privacy-preserving algorithm (MTPPA) using simulated annealing for optimal k-anonymity set selection.

Main Results:

• The MTPPA algorithm effectively incorporates user attributes into the k-anonymity set construction.
• Experimental results demonstrate a significant reduction in privacy disclosure probability (approx. 40% lower) compared to existing methods.
• The proposed method achieves comparable service quality to traditional approaches.

Conclusions:

• The MTPPA algorithm offers a more robust solution for trajectory privacy preservation in LBSs.
• Considering user attributes is essential for strengthening trajectory privacy.
• This research contributes to more secure and trustworthy LBS applications.