Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation

• URL: http://arxiv.org/abs/2512.00638v1
• Date: Sat, 29 Nov 2025 21:23:57 GMT
• Title: Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation
• Authors: Timur Sattarov, Marco Schreyer, Damian Borth,
• Abstract summary: We introduce DP-FinDiff, a differentially private diffusion framework for synthesizing mixed-type tabular data.<n> DP-FinDiff employs embedding-based representations for categorical features, reducing encoding overhead and scaling to high-dimensional datasets.<n>On financial and medical datasets, DP-FinDiff achieves 16-42% higher utility than DP baselines at comparable privacy levels.
• Score: 8.857443660746979
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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