A local differential privacy extension scheme for sensitive locations of hotspot areas
Ruowei Gui, Xingjun Zhang, Xiaolin Gui · 2024
With the popularization of smartphones with GPS, user information attached to the locations is facing the risk of leakage. In the real world, even if the locations of a user are well protected, attackers can also mine user privacy by analyzing the user correlations in common hotspots. To solve the above problems, we propose a local differential privacy extension scheme in hotspot areas. In this scheme, we firstly obtain the user’s hotspots by mining the user trajectories based on the sliding time window, and then, we extract the correlation degrees among users from these hotspots using the Jaccard correlation coefficient, and finally we introduce the personalization correlation sensitivity to extend the local differential privacy so as to protect sensitive locations in hotspot areas. Experiments show that, compared with existing methods, our scheme can improve the usability of trajectories up to $\mathbf{6. 6 4 \%}$ at the same privacy level.