A Utility-Aware Privacy-Preserving Method for Trajectory Publication.

Area of Science:

• Computer Science
• Data Privacy
• Geographic Information Systems

Background:

• Individual privacy is critical for trajectory data publication, yet maintaining data utility for urban planning and transportation analysis remains a challenge.
• Existing methods often overlook semantic trajectory characteristics (origin, destination, path), hindering task-specific utility measurements and the privacy-utility balance.
• A gap exists in methods that can flexibly balance privacy and utility by considering the inherent semantics of trajectory data.

Purpose of the Study:

• To propose an interactive trajectory publishing approach that prioritizes flexible utility considerations.
• To enable data owners to specify task-specific utility measurements by processing trajectory points based on their semantics.
• To achieve a delicate balance between individual privacy and trajectory utility for diverse applications.

Main Methods:

• Decoupling trajectories into origin-destination (OD) and path components for distinct processing.
• Generalizing OD regions to satisfy k-anonymity requirements for privacy.
• Sanitizing path components within OD groups using road-network-aware differential privacy.
• Developing an interactive visual interface to incorporate human knowledge into the privacy scheme.

Main Results:

• The proposed approach effectively processes trajectory points according to their semantics to meet varied utility requirements.
• Experiments on real-world urban datasets validate the effectiveness of the semantic-driven privacy-preserving method.
• The interactive interface facilitates exploration and comprehension of privacy-preserving solutions.

Conclusions:

• The developed interactive trajectory publishing approach successfully balances privacy and utility by leveraging semantic characteristics.
• The method offers a flexible framework for privacy-preserving trajectory data publication, adaptable to different utility needs.
• This work advances the field by integrating semantic understanding and human-in-the-loop interaction for robust location privacy.