Evaluating the utility of human mobility data under local differential privacy

In this paper, we evaluate the impact of local differential privacy (LDP) on the utility of human mobility data obtained from mobile location services. Specifically, we focus our study on visit data, which consist of user-level information on visited locations. This includes the duration of each visit and its category, such as restaurant or department store. The purpose of LDP is to protect sensitive information in visit records by introducing properly calibrated noise, while still allowing the extraction of useful statistics. To evaluate our approach, we study how different levels of privacy budget ϵ impact the utility of the data. The utility is determined by the estimation accuracy for different statistics of interest, such as the number of visits and the average visit duration for each category. We conduct our evaluation on a visits dataset including records from over 20 million mobile devices. Our findings indicate that the number of visits to popular categories can be accurately estimated even at strong privacy levels (ϵ = 1). The estimation of the average visit duration is generally less precise, but it remains feasible under less stringent privacy levels (ϵ ≥ 2 or ϵ ≥ 4, depending on the application at hand).