用神经网络提升隐私保护表格数据生成，尤其擅长复杂相关数据。
Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis
- 将统计模型的设计融入神经网络，自适应选择边际分布进行训练
- 在密集相关数据上误差降低26%，超越现有最佳方法
- 速度比最优统计方法快7倍，适合高维复杂表格数据
在差分隐私表格数据合成中，传统观点认为统计模型优于基于神经网络（NN）的方法。但我们认为这一结论不完整，忽略了高密度相关数据集的挑战——复杂的依赖关系会压垮统计模型。在此类场景下，神经网络因能直接从样本中学习复杂分布而更具优势。然而，现有基于神经网络的算法仍存在显著局限。为此，我们提出 MargNet，将统计模型的成功设计融入神经网络：采用自适应边际选择策略，并训练神经网络生成符合选定边际的数据。在稀疏相关数据集上，该方法性能接近最优统计方法，平均提速7倍；更重要的是，在密集相关数据集上，MargNet达到新基准，使保真度误差降低高达26%。代码已开源。
原文摘要 · Abstract (English)
In differentially private (DP) tabular data synthesis, the consensus is that statistical models are better than neural network (NN)-based methods. However, we argue that this conclusion is incomplete and overlooks the challenge of densely correlated datasets, where intricate dependencies can overwhelm statistical models. In such complex scenarios, neural networks are more suitable due to their capacity to fit complex distributions by learning directly from samples. Despite this potential, existing NN-based algorithms still suffer from significant limitations. We therefore propose MargNet, incorporating successful algorithmic designs of statistical models into neural networks. MargNet applies an adaptive marginal selection strategy and trains the neural networks to generate data that conforms to the selected marginals. On sparsely correlated datasets, our approach achieves utility close to the best statistical method while offering an average 7$\times$ speedup over it. More importantly, on densely correlated datasets, MargNet establishes a new state-of-the-art, reducing fidelity error by up to 26\% compared to the previous best. We release our code on GitHub.\footnote{https://github.com/KaiChen9909/margnet}
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