Trust-Aware Benchmarking of GAN, VAE, and Diffusion Models for Synthetic Data in Image and Tabular Domains
Abstract
Abstract Synthetic data generation using generative AI models offers a promising solution to challenges in data availability, privacy, and fairness. However, comparative insights across model families and data modalities remain limited, especially when trust-related dimensions such as fairness, privacy, and efficiency are considered alongside fidelity.This paper presents a trust-aware benchmarking study across both visual (CIFAR-10, MNIST) and tabular (Adult Income) domains, evaluating representative baselines—vanilla GAN/WGAN-GP, standard and β-VAE, DDPM and classifier-guided DDPM, and hybrids such as VAE-GAN and Latent Diffusion. Advanced models including StyleGAN2/3, BigGAN, CTGAN, Diffusion Transformers, and GigaGAN are acknowledged to situate the findings within the evolving landscape of foundation-scale generators.Performance is assessed using a multi-objective framework that integrates fidelity (FID, precision/recall), fairness (demographic parity), privacy leakage resistance, and computational efficiency. Results show that hybrid latent diffusion models achieve near-diffusion fidelity (FID 10.2 vs. 8.5 on CIFAR-10; 7.8 vs. 6.2 on MNIST) while reducing sampling time by over 70%. On tabular data, hybrids balance accuracy (84.7%) and fairness (0.93), whereas classical GANs and VAEs exhibit trade-offs between fidelity, efficiency, and fairness.To the best of our knowledge, this is the first study to benchmark GANs, VAEs, diffusion, and hybrid models across both image and…
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