The generation of synthetic tabular data that preserves differential privacy
is a problem of growing importance. While traditional marginal-based methods
have achieved impressive results, recent work has shown that deep
learning-based approaches tend to lag behind. In this work, we present
Differentially-Private TaBular AutoRegressive Transformer (DP-TBART), a
transformer-based autoregressive model that maintains differential privacy and
achieves performance competitive with marginal-based methods on a wide variety
of datasets, capable of even outperforming state-of-the-art methods in certain
settings. We also provide a theoretical framework for understanding the
limitations of marginal-based approaches and where deep learning-based
approaches stand to contribute most. These results suggest that deep
learning-based techniques should be considered as a viable alternative to
marginal-based methods in the generation of differentially private synthetic
tabular data.

维护差分隐私并在各种数据集上表现出与基于边际的方法相竞争的性能，甚至能够在某些情况下超越现有技术方法，我们提出了基于 Transformer 的差分隐私表格自回归模型 (DP-TBART)，并提供了理论框架来理解基于边际的方法的局限性以及深度学习方法的贡献。这些结果表明，在生成差分隐私合成表格数据中，应将基于深度学习的技术视为可行的替代方法。