A Range Query Scheme for Spatial Data with Shuffled Differential Privacy

The existing high-dimensional or multi-dimensional geographic spatial datasets have a large amount of data. When third-party servers collect and publish them, privacy protection is required to prevent sensitive information from being leaked. Local differential privacy can be used to protect location-sensitive information during range queries. However, the accuracy of a range query based on local differential privacy is affected by the distribution and density of spatial data. Based on this, aiming at the distribution and density characteristics of data, we designed a ${}_{dp}KD$ tree that supports high-precision range queries with shuffled differential privacy, and designed an algorithm KDRQ for range queries based on shuffled differential privacy. First, we employed the ${}_{dp}KD$ to divide the data. Then, we shuffled the data based on $SR{R}_Q$ and reconstructed the tree. Finally, we used the SDRQ algorithm for the response range query. The experimental results show that the query accuracy of the KDRQ algorithm was at least 1–4.5 times higher than that of the existing algorithms RAPPOR, PSDA and GT-R under the same privacy budget.