ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
This addresses privacy concerns in spatiotemporal data for applications like personal communication and financial transactions, but it is incremental as it builds on existing Graph-GAN and differential privacy methods.
The paper tackles the problem of generating privacy-protected spatiotemporal data for edge devices by proposing a Graph-GAN-based model with spatial and temporal attention blocks and a spatiotemporal deconvolution structure, achieving differential privacy while maintaining data utility, with experiments on three real-world datasets showing competitive performance in prediction models trained on the generated data.
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.