Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models

Understand

Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis.

• However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions.
• In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race.
• The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples.