LBS user location privacy protection scheme based on trajectory similarity

Area of Science:

• Computer Science
• Information Security
• Data Privacy

Background:

• Conventional methods for LBS user privacy often introduce noise, leading to data distortion or insufficient protection.
• Balancing privacy protection with data utility remains a significant challenge in location-based services.

Purpose of the Study:

• To propose a novel LBS user location privacy protection scheme based on trajectory similarity (DPTS).
• To enhance privacy protection without compromising data utility or algorithmic efficiency.
• To address the limitations of existing methods regarding noise levels and data distortion.

Main Methods:

• Introduced a privacy protection method during location data collection and publication.
• Superimposed a differential privacy algorithm based on a trajectory prefix tree onto a false location replacement algorithm using trajectory similarity.
• Developed an LBS-based Difference Privacy Protection Algorithm that replaces original trajectories with least similar fake traces.

Main Results:

• The proposed DPTS algorithm effectively protects user privacy by replacing original trajectories with less similar fake ones.
• The algorithm establishes a prefix tree and adds noise to positional frequency for enhanced protection.
• Simulation results demonstrate improved data utility, increased noise levels, and enhanced operational efficiency.

Conclusions:

• The DPTS scheme offers a reliable privacy protection solution for LBS users.
• It successfully suppresses data distortion rates while increasing noise levels for better privacy.
• The approach improves algorithm efficiency and significantly reduces the risk of sensitive location information leakage.