D²-PSD: Dynamic Differentially-Private Spatial Decomposition in Collaboration With Edge Server

Abstract

Spatio-temporal data possess intrinsic values, reflecting the spatial and temporal features of people’s behaviors. Due to the sensitive nature of this data (e.g., workplace, residence, school locations), privacy protection is essential when collecting spatio-temporal data. Local Differential Privacy (LDP) protocol has gained attention as a method for protecting privacy on data-collecting devices. LDP protocol can make each data indistinguishable but inevitably destroys spatial/temporal characteristics as well. In this paper, we propose a novel method enabling LDP protocol to preserve spatial/temporal trends on privacy protection. If we collect data from users with similar behavior, it is difficult to uniquely identify users from the beginning. In short, processing privacy protection for each user with similar behavior allow us to minimize the removal of intrinsic values by LDP protocol. Our method, termed Dynamic Differentially-Private Spatial Decomposition (D2-PSD), dynamically adjusts and controls the strength of privacy protection (privacy budget) for each group of users exhibiting similar spatial and temporal trends. This allows users to be indistinguishable from each other within a group while preserving spatial and temporal trends across groups. All groups will have a different privacy budget, but the sum of the entire group keeps a constant privacy budget. Even if group with different protection strengths are mixed, privacy is protected for the sum of the group, and our proposed method can always guarantee a constant protection strength. Experimental results demonstrate that our method retains the intrinsic spatial and temporal trends in spatio-temporal data while maintaining robust privacy protection across the entire dataset, thanks to the D2-PSD approach. Specifically, in the most similar groups, D2-PSD reduced the MAE by up to 75% compared to standard LDP, while maintaining an equivalent strength of privacy protection.

Keywords

• Local differential privacy
• private spatial decomposition
• spatio-temporal data
• geospatial clustering
• edge computing