Toward Answering Federated Spatial Range Queries Under Local Differential Privacy

Abstract

Federated analytics (FA) over spatial data with local differential privacy (LDP) has attracted considerable research attention recently. Existing solutions for this problem mostly employ a uniform grid (UG) structure, which recursively decomposes the whole spatial domain into fine-grained regions in the distributed setting. In each round, the sampled clients perturb their locations using a random response mechanism with a fixed probability. This approach, however, cannot encode the client’s location effectively and will lead to ill-suited query results. To address the deficiency of existing solutions, we propose LDP-FSRQ, a spatial range query algorithm that relies on a hybrid spatial structure composed of the UG and quad-tree with nonuniform perturbation (NUP) probability to encode and perturb clients’ locations. In each iteration of LDP-FSRQ, each client adopts the quad-tree to encode his/her location into a binary string and uses four local perturbation mechanisms to protect the encoded string. Then, the collector prunes the quad-tree of the current round according to the clients’ reports and shares the pruned tree with the clients of the next round. We demonstrate the application of LDP-FSRQ on Beijing, Landmark, Check-in, and NYC datasets, and the experimental results show that our approach outperforms its competitors in terms of queries’ utility.