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

Wei Shao, Rongyi Zhu, Cai Yang, Chandra Thapa, Muhammad Ejaz Ahmed, Seyit Camtepe, Rui Zhang, DuYong Kim, Hamid Menouar, Flora D. Salim

TL;DR

ST-DPGAN introduces a privacy-preserving framework for spatiotemporal data generation by integrating differential privacy with a Graph-GAN. The generator uses a transConv1d module to map 1-D Gaussian noise to a $T \times N$ spatiotemporal representation, while the discriminator employs spatial and temporal attention over a graph-embedded input, with DP-SGD enforcing privacy guarantees. Across three real-world datasets, ST-DPGAN and its Attn variant achieve superior data quality (lower MSE/MAE) than baselines like DPGAN and WGAN under varying privacy budgets, with ablation studies confirming the critical roles of transConv1d and graph embedding. The work demonstrates that privacy-protected synthetic spatiotemporal data can retain substantial utility for downstream predictive tasks, enabling safer data sharing and analysis in sensitive domains.

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.

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

TL;DR

ST-DPGAN introduces a privacy-preserving framework for spatiotemporal data generation by integrating differential privacy with a Graph-GAN. The generator uses a transConv1d module to map 1-D Gaussian noise to a spatiotemporal representation, while the discriminator employs spatial and temporal attention over a graph-embedded input, with DP-SGD enforcing privacy guarantees. Across three real-world datasets, ST-DPGAN and its Attn variant achieve superior data quality (lower MSE/MAE) than baselines like DPGAN and WGAN under varying privacy budgets, with ablation studies confirming the critical roles of transConv1d and graph embedding. The work demonstrates that privacy-protected synthetic spatiotemporal data can retain substantial utility for downstream predictive tasks, enabling safer data sharing and analysis in sensitive domains.

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.
Paper Structure (15 sections, 2 theorems, 15 equations, 2 figures, 3 tables, 1 algorithm)

This paper contains 15 sections, 2 theorems, 15 equations, 2 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Preliminary
  4. Methodology
  5. Generator
  6. transConv1d
  7. Discriminator
  8. Model Summary
  9. Experiments
  10. Dataset
  11. Data Preprocessing
  12. Experimental Settings
  13. Results and Analysis
  14. Ablation Study
  15. Conclusion

Key Result

Theorem 1

Let the initial random data be $X$ of size $1 \times N$, where each entry is $x_{j}$, and let the kernel be $K$ of size $s\times s$, where each entry is $k_{ij}$. Assume $x_{j}\stackrel{i.i.d}{\sim} \mathcal{N}(\mu_1,\sigma_1^2)$, $k_{ij}\stackrel{i.i.d}{\sim} \mathcal{N}(\mu_2,\sigma_2^2)$, and $x_ where $Q_1,Q_2\sim \chi_m^2$, $G\sim \mathcal{N}(0,\frac{m^2(\sigma_1^2+\sigma_2^2)(\mu_1^2+\mu_2^2

Figures (2)

  • Figure 1: A hostile individual can extract sensitive information from original spatiotemporal data. Our proposed method enables the generation of privacy-protected data while maintaining good data quality. Our method serves as a fundamental study for applying spatiotemporal analysis techniques on a large scale.
  • Figure 2: Architecture of ST-DPGAN.

Theorems & Definitions (3)

  • Theorem 1
  • proof
  • Lemma 2