Differentially Private Normalizing Flows for Synthetic Tabular Data Generation

Authors

  • Jaewoo Lee University of Georgia
  • Minjung Kim Samsung SDS
  • Yonghyun Jeong Samsung SDS
  • Youngmin Ro Samsung SDS

DOI:

https://doi.org/10.1609/aaai.v36i7.20697

Keywords:

Machine Learning (ML)

Abstract

Normalizing flows have shown to be a promising approach to deep generative modeling due to their ability to exactly evaluate density --- other alternatives either implicitly model the density or use approximate surrogate density. In this work, we present a differentially private normalizing flow model for heterogeneous tabular data. Normalizing flows are in general not amenable to differentially private training because they require complex neural networks with larger depth (compared to other generative models) and use specialized architectures for which per-example gradient computation is difficult (or unknown). To reduce the parameter complexity, the proposed model introduces a conditional spline flow which simulates transformations at different stages depending on additional input and is shared among sub-flows. For privacy, we introduce two fine-grained gradient clipping strategies that provide a better signal-to-noise ratio and derive fast gradient clipping methods for layers with custom parameterization. Our empirical evaluations show that the proposed model preserves statistical properties of original dataset better than other baselines.

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Published

2022-06-28

How to Cite

Lee, J., Kim, M., Jeong, Y., & Ro, Y. (2022). Differentially Private Normalizing Flows for Synthetic Tabular Data Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7345-7353. https://doi.org/10.1609/aaai.v36i7.20697

Issue

Section

AAAI Technical Track on Machine Learning II