Improving Data Utility in Privacy-Preserving Location Data Collection via Adaptive Grid Partitioning

Abstract

The widespread availability of GPS-enabled devices and advances in positioning technologies have significantly facilitated collecting user location data, making it an invaluable asset across various industries. As a result, there is an increasing demand for the collection and sharing of these data. Given the sensitive nature of user location information, considerable efforts have been made to ensure privacy, with differential privacy (DP)-based schemes emerging as the most preferred approach. However, these methods typically represent user locations on uniformly partitioned grids, which often do not accurately reflect the true distribution of users within a space. Therefore, in this paper, we introduce a novel method that adaptively adjusts the grid in real-time during data collection, thereby representing users on these dynamically partitioned grids to enhance the utility of the collected data. Specifically, our method directly captures user distribution during the data collection process, eliminating the need to rely on pre-existing user distribution data. Experimental results with real datasets show that the proposed scheme significantly enhances the utility of the collected location data compared to the existing method.