  Synthetic data generation is a fundamental task for many data management and
data science applications. Spatial data is of particular interest, and its
sensitive nature often leads to privacy concerns. We introduce GeoPointGAN, a
novel GAN-based solution for generating synthetic spatial point datasets with
high utility and strong individual level privacy guarantees. GeoPointGAN's
architecture includes a novel point transformation generator that learns to
project randomly generated point co-ordinates into meaningful synthetic
co-ordinates that capture both microscopic (e.g., junctions, squares) and
macroscopic (e.g., parks, lakes) geographic features. We provide our privacy
guarantees through label local differential privacy, which is more practical
than traditional local differential privacy. We seamlessly integrate this level
of privacy into GeoPointGAN by augmenting the discriminator to the point level
and implementing a randomized response-based mechanism that flips the labels
associated with the 'real' and 'fake' points used in training. Extensive
experiments show that GeoPointGAN significantly outperforms recent solutions,
improving by up to 10 times compared to the most competitive baseline. We also
evaluate GeoPointGAN using range, hotspot, and facility location queries, which
confirm the practical effectiveness of GeoPointGAN for privacy-preserving
querying. The results illustrate that a strong level of privacy is achieved
with little-to-no adverse utility cost, which we explain through the
generalization and regularization effects that are realized by flipping the
labels of the data during training.
