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arXiv · 2512.00638

Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation

Timur Sattarov·Marco Schreyer·Damian Borth
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Abstract

We introduce DP-FinDiff, a differentially private diffusion framework for synthesizing mixed-type tabular data. DP-FinDiff employs embedding-based representations for categorical features, reducing encoding overhead and scaling to high-dimensional datasets. To adapt DP-training to the diffusion process, we propose two privacy-aware training strategies: an adaptive timestep sampler that aligns updates with diffusion dynamics, and a feature-aggregated loss that mitigates clipping-induced bias. Together, these enhancements improve fidelity and downstream utility without weakening privacy guarantees. On financial and medical datasets, DP-FinDiff achieves 16-42% higher utility than DP baselines at comparable privacy levels, demonstrating its promise for safe and effective data sharing in sensitive domains.

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Timur Sattarov, Marco Schreyer, Damian Borth. 2025-11-29. Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation. https://arxiv.org/abs/2512.00638

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