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.

翻译：时空数据广泛存在于各类边缘设备中，例如个人通信和金融交易设备。近年来的进展引发了将时空分析与大规模语言模型相结合的日益增长的兴趣。然而，时空数据通常包含敏感信息，使其不适合向第三方公开访问。为解决这一挑战，我们提出一种基于Graph-GAN的模型，用于生成隐私保护的时空数据。我们的方法在判别器中引入空间和时间注意力模块，并在生成器中采用时空反卷积结构。这些改进使得模型能够在高斯噪声下高效训练，从而实现差分隐私。在三个真实世界时空数据集上进行的大量实验验证了我们模型的有效性。我们的方法在提供隐私保证的同时，保持了数据实用性。基于我们生成数据训练的预测模型，其性能与基于原始数据训练的模型相比具有竞争力。