Perturb and optimize users' location privacy using geo-indistinguishability and location semantics.

Area of Science:

• Computer Science
• Information Security
• Geographic Information Systems

Background:

• Location-based services (LBS) offer valuable functionalities but pose significant privacy risks due to potential location data leakage.
• Existing location privacy protection methods can be vulnerable to semantic inference attacks, where attackers exploit location characteristics to deduce real user locations.

Purpose of the Study:

• To propose a user-side location perturbation and optimization algorithm that enhances location privacy in LBS.
• To mitigate semantic inference attacks and improve the quality of LBS by balancing privacy and utility.

Main Methods:

• Developed a location perturbation algorithm integrating geo-indistinguishability (planar Laplace mechanism) with location semantics and time characteristics.
• Utilized linear programming to select an optimal perturbed location that minimizes the loss of LBS quality.

Main Results:

• The proposed method reduced perturbation distance by approximately 37% compared to state-of-the-art techniques.
• Demonstrated significantly lower location semantic similarity, indicating enhanced privacy.
• Improved region counting query accuracy by around 40%.

Conclusions:

• The proposed algorithm effectively protects user location privacy against semantic inference attacks.
• It offers a superior balance between location privacy and LBS utility compared to existing methods.
• This approach enhances the security and reliability of location-based services.