  Spatiotemporal data is prevalent in a wide range of edge devices, such as
those used in personal communication and financial transactions. Recent
advancements have sparked a growing interest in integrating spatiotemporal
analysis with large-scale language models. However, spatiotemporal data often
contains sensitive information, making it unsuitable for open third-party
access. To address this challenge, we propose a Graph-GAN-based model for
generating privacy-protected spatiotemporal data. Our approach incorporates
spatial and temporal attention blocks in the discriminator and a spatiotemporal
deconvolution structure in the generator. These enhancements enable efficient
training under Gaussian noise to achieve differential privacy. Extensive
experiments conducted on three real-world spatiotemporal datasets validate the
efficacy of our model. Our method provides a privacy guarantee while
maintaining the data utility. The prediction model trained on our generated
data maintains a competitive performance compared to the model trained on the
original data.
