Utility-optimized location privacy scheme with geo-indistinguishability
Ke Zhu, Pengfei Yu, Xuehong Chen · Third International Conference on Computer Science and Communication Technology (ICCSCT 2022) · 2022
Geographic location privacy protection is an important research content of privacy protection in recent years. Local differential privacy mechanism is one of the mainstream geographic location privacy protection models. among ε-Geoindistinguishability has become the basic research standard of local differential privacy geographic location protection scheme using distance measurement. Combining the ideas of ε-Geo-indistinguishability and Utility-Optimized Local Differential Privacy, we propose a differential privacy concept Utility-Optimized- ε-Geo-indistinguishability which meets the different degrees of privacy protection in different geographical locations. This is the first time in the field of local differential privacy geographic location protection based on distance measurement. At the same time, we propose a localized differential privacy mechanism called Utility-Optimized Planar Laplace Mechanism that can meet UtilityOptimized-𝜀 -Geo-indistinguishability. Theoretical analysis and experiments based on real data sets show that the experimental effect of our proposed mechanism is better than the existing local differential privacy geographic protection mechanism.