ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation

• URL: http://arxiv.org/abs/2406.03404v1
• Date: Tue, 4 Jun 2024 04:43:54 GMT
• Title: ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
• Authors: Wei Shao, Rongyi Zhu, Cai Yang, Chandra Thapa, Muhammad Ejaz Ahmed, Seyit Camtepe, Rui Zhang, DuYong Kim, Hamid Menouar, Flora D. Salim,
• Abstract summary: We propose a Graph-based model for generating privacy-protected data. Experiments conducted on three real-worldtemporal datasets validate the efficacy of our model. The prediction model trained on our generated data maintains a competitive edge compared to the model trained on the original data.
• Score: 19.18074489351738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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