  A spatial data federation is a collection of data owners (e.g., a consortium
of taxi companies), and collectively it could provide better location-based
services (LBS). For example, car-hailing services over a spatial data
federation allow end users to easily pick the best offers. We focus on the
range counting queries, which are primitive operations in spatial databases but
received little attention in related research, especially considering the
privacy requirements from data owners, who are reluctant to disclose their
proprietary data. We propose a grouping-based technical framework named
FedGroup, which groups data owners without compromising privacy, and achieves
superior query accuracy (up to 50% improvement) as compared to directly
applying existing privacy mechanisms achieving Differential Privacy (DP). Our
experimental results also demonstrate that FedGroup runs orders-of-magnitude
faster than traditional Secure Multiparty Computation (MPC) based method, and
FedGroup even scales to millions of data owners, which is a common setting in
the era of ubiquitous mobile devices.

Databases