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

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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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-05-22
DOI
https://doi.org/10.1109/jiot.2025.3568863
Primary Topic
3D Modeling in Geospatial Applications
Type
article
Field-Weighted Citation Impact
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article

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

Chandra Thapa, Du Yong Kim, Muhammad Ejaz Ahmed, Yang Cai et al.
IEEE Internet of Things Journal
3D Modeling in Geospatial Applications
article

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

Chandra Thapa, Du Yong Kim, Muhammad Ejaz Ahmed, Yang Cai, Hamid Menouar, Seyit Ahmet Camtepe, Flora Dilys Salim, Rui Zhang, Wei Shao, Rongyi Zhu
article en

Abstract

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.

IEEE Internet of Things JournalVol. 12(15)
Australian National University (AU), Commonwealth Scientific and Industrial Research Organisation (AU), The University of Sydney (AU), UNSW Sydney (AU), Qatar Mobility Innovations Center (QA), MIT University (MK), Data61 (AU), Huazhong University of Science and Technology (CN), University of Rochester (US), RMIT University (AU)
Openalex Percentile: Top 89%
3D Modeling in Geospatial Applications
DOISourceOpenAlex
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