A fine granularity based user collaboration algorithm for location privacy protection.

Area of Science:

• Computer Science
• Information Security
• Data Privacy

Background:

• Location trajectories contain sensitive spatial-temporal information, posing privacy risks.
• Existing query division algorithms struggle with fixed block sizes, enabling adversaries to correlate data.
• The need for robust methods to obscure location data and prevent trajectory reconstruction is critical.

Purpose of the Study:

• To propose a novel fine granularity block division algorithm for enhanced location privacy protection.
• To address the limitations of previous methods in handling variable block sizes and adversary correlation.
• To improve user privacy by making location information blocks indistinguishable.

Main Methods:

• Implementing a fine granularity block division algorithm based on granularity measurement and layer division.
• Utilizing collaborative users to exchange information blocks and eliminate size differences.
• Dividing query contexts into small, indistinguishable information blocks.

Main Results:

• The proposed algorithm effectively obfuscates query context by creating fine granularity blocks.
• Collaborative sharing of blocks makes it difficult for adversaries to distinguish or correlate discrete locations.
• Theoretical analysis and experimental verification confirm the algorithm's superiority in protecting location privacy.

Conclusions:

• The fine granularity block division algorithm offers a robust solution for safeguarding user location privacy.
• Indistinguishable information blocks and collaborative sharing are key to preventing trajectory inference.
• This approach significantly enhances data security against adversaries attempting to correlate location data.