提出一种融合贝叶斯网络的私有生成模型，更好平衡数据隐私与真实度。
PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure
- 用序列生成器捕捉变量间复杂依赖，结合贝叶斯网络结构自适应正则化。
- 理论证明参数距离、变量选择误差和Wasserstein距离均随迭代收敛降低。
- 在合成与真实数据集上均优于现有方法，适合高隐私要求的表格数据生成。
我们重新审视了差分隐私下的合成数据生成问题。为解决基于边缘分布方法的核心局限，提出私有自适应生成对抗网络（PrAda-GAN），其融合了基于GAN与基于边缘分布方法的优势。该方法采用序列生成架构以捕捉变量间的复杂依赖，并自适应地正则化学习到的结构，以促进底层贝叶斯网络的稀疏性。理论上，我们建立了参数距离、变量选择误差和Wasserstein距离的递减界。分析表明，利用依赖稀疏性可显著提升收敛速度。在合成与真实世界数据集上的实验表明，PrAda-GAN在隐私-效用权衡方面优于现有表格数据合成方法。
原文摘要 · Abstract (English)
We revisit the problem of generating synthetic data under differential privacy. To address the core limitations of marginal-based methods, we propose the Private Adaptive Generative Adversarial Network with Bayes Network Structure (PrAda-GAN), which integrates the strengths of both GAN-based and marginal-based approaches. Our method adopts a sequential generator architecture to capture complex dependencies among variables, while adaptively regularizing the learned structure to promote sparsity in the underlying Bayes network. Theoretically, we establish diminishing bounds on the parameter distance, variable selection error, and Wasserstein distance. Our analysis shows that leveraging dependency sparsity leads to significant improvements in convergence rates. Empirically, experiments on both synthetic and real-world datasets demonstrate that PrAda-GAN outperforms existing tabular data synthesis methods in terms of the privacy-utility trade-off.
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