User Identification across Asynchronous Mobility Trajectories.

Area of Science:

• Computer Science
• Data Science
• Security and Privacy

Background:

• The proliferation of location-based services generates vast amounts of mobility data.
• Identifying individuals using mobile trajectory data, particularly asynchronous data, is crucial for social security.
• Existing methods face challenges in computational complexity and efficiency.

Purpose of the Study:

• To propose an effective and efficient user identification method using mobility data.
• To reduce computational complexity by focusing on frequently visited regions (TOP-N regions).
• To evaluate the performance of different trajectory similarity metrics for user identification.

Main Methods:

• The proposed method identifies TOP-N regions with the highest density of trajectory points.
• Three distinct trajectory similarity metrics are employed for matching user tracks.
• Experiments were conducted on two real-world GPS trajectory datasets: GeoLife and Cabspotting.

Main Results:

• The TOP-N region approach significantly reduces computational complexity.
• The evaluated similarity metrics demonstrate effectiveness in matching trajectories from the same user.
• Extensive experiments confirm the substantial effectiveness and efficiency of the proposed method.

Conclusions:

• The TOP-N region-based user identification method offers a practical solution for analyzing mobility data.
• This approach enhances the feasibility of user identification for social security applications.
• The method provides a computationally efficient and effective means for identifying individuals from GPS trajectory data.