DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation

• URL: http://arxiv.org/abs/2307.10430v1
• Date: Wed, 19 Jul 2023 19:40:21 GMT
• Title: DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation
• Authors: Rodrigo Castellon, Achintya Gopal, Brian Bloniarz, David Rosenberg
• Abstract summary: We present Differentially-Private TaBular AutoRegressive Transformer (DP-TBART), a transformer-based autoregressive model that maintains differential privacy. We provide a theoretical framework for understanding the limitations of marginal-based approaches and where deep learning-based approaches stand to contribute most.
• Score: 1.4418363806859886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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