  Mobile apps and location-based services generate large amounts of location
data that can benefit research on traffic optimization, context-aware
notifications and public health (e.g., spread of contagious diseases). To
preserve individual privacy, one must first sanitize location data, which is
commonly done using the powerful differential privacy (DP) concept. However,
existing solutions fall short of properly capturing density patterns and
correlations that are intrinsic to spatial data, and as a result yield poor
accuracy. We propose a machine-learning based approach for answering
statistical queries on location data with DP guarantees. We focus on countering
the main source of error that plagues existing approaches (namely, uniformity
error), and we design a neural database system that models spatial datasets
such that important density and correlation features present in the data are
preserved, even when DP-compliant noise is added. We employ a set of neural
networks that learn from diverse regions of the dataset and at varying
granularities, leading to superior accuracy. We also devise a framework for
effective system parameter tuning on top of public data, which helps
practitioners set important system parameters without having to expend scarce
privacy budget. Extensive experimental results on real datasets with
heterogeneous characteristics show that our proposed approach significantly
outperforms the state of the art.
