TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling

2026-06-30

AI Summary

This work addresses critical limitations in existing tabular data generation methods, which often fail to simultaneously achieve high generation quality, robust privacy preservation, and deployment efficiency due to inadequate adaptive modeling, multidimensional evaluation, and usability. To overcome these challenges, we propose TDGT, a web-based end-to-end framework that innovatively integrates an Adaptive Bayesian Mixture Synthesizer (ABMS) with a Variational Autoencoder (VAE) to enable high-fidelity modeling of heterogeneous tabular data. TDGT incorporates CUDA-accelerated clustering and fitting algorithms and, for the first time, unifies eleven statistical fidelity metrics, k-anonymity, leakage rate, and interactive visualizations within a single cohesive system. Empirical evaluations across diverse domains—including healthcare, socioeconomic, and cybersecurity datasets—demonstrate that TDGT consistently produces synthetic data that preserves statistical consistency while ensuring strong privacy guarantees.

Abstract

The growing demand for privacy-preserving data sharing has positioned synthetic data generation as a critical component of responsible AI workflows. Despite notable advances in generative modeling, existing solutions often lack integration of adaptive generation strategies, multi-metric evaluation, and accessible end-to-end generators within a unified web-based toolkit. In this work, we introduce TDGT (Tabular Data Generation Toolkit), a web-based toolkit for synthetic tabular data generation and fidelity assessment. TDGT introduces the Adaptive Bayesian Mixture Synthesizer (ABMS), a novel algorithm that autonomously determines the optimal number of mixture components through iterative cluster quality optimization, eliminating the need for manual hyperparameter configuration. Building upon ABMS, we further propose VAE-ABMS, a hybrid architecture that couples Variational Autoencoder-based latent space learning with adaptive Bayesian mixture synthesis, enabling high-fidelity generation of complex, nonlinear tabular distributions. For large-scale scenarios, TDGT provides a GPU-accelerated variant of ABMS leveraging CUDA-based k-means clustering and Gaussian mixture fitting. Synthetic data fidelity is assessed through eleven statistical fidelity metrics spanning distributional divergence, structural correlation, and sample-level similarity, complemented by privacy risk indicators including k-anonymity scoring and disclosure rate estimation. The web-based toolkit supports a real-time streaming interface with interactive Plotly-based visualizations. TDGT is assessed across datasets from healthcare, socioeconomic modeling, and cybersecurity domains, demonstrating consistent generation fidelity and statistical coherence across heterogeneous feature types and data scales.

Problem

• synthetic data generation
• tabular data
• privacy-preserving
• generative modeling
• adaptive generation

Innovation

• Adaptive Bayesian Mixture Synthesizer
• VAE-ABMS
• GPU-accelerated generative modeling
• synthetic tabular data
• privacy-preserving data generation