LDPart: Effective Location-Record Data Publication via Local Differential Privacy

Driven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available. Publishing such data is of vital importance to the advancement of a wide spectrum of applications, such as marketing analysis, targeted advertising, and urban planning. However, the data collection may pose considerable threats to the individuals privacy. Local differential privacy (LDP) has recently emerged as a strong privacy standard for collecting sensitive information from users. Due to the inherent high dimensionality, it is particularly challenging to publish the location-record data under LDP. In this paper, we propose LDPart, a probabilistic top-down partitioning algorithm to effectively generate a sanitized location-record data. Our approach employs a carefully designed partition tree model to extract essential information in terms of location records. Furthermore, it also makes use of a novel adaptive user allocation scheme and a series of optimization techniques to improve the accuracy of the released data. The extensive experiments conducted on real-world datasets demonstrate that the proposed approach maintains high utility while providing privacy guarantees.