No Free Lunch for Synthetic Images under Data Scarcity Conditions

Researchers evaluated trade-offs between fidelity, privacy, and utility in synthetic image generation across VAE, GAN, and DDPM models under data scarcity conditions. The study reveals that GANs and DDPMs maintain performance better than VAEs when differential privacy mechanisms are applied, suggesting no single generative model excels across all three dimensions simultaneously.

This research addresses a fundamental challenge in machine learning: generating high-quality synthetic data while protecting privacy, particularly when training data is limited. The study's evaluation framework is significant because it moves beyond single-metric assessments to examine how generative models perform across three competing objectives—fidelity, privacy, and utility. Traditional benchmarking often prioritizes one dimension at the expense of others, but this multidimensional approach reveals genuine trade-offs that practitioners must navigate.

The differential performance of VAE, GAN, and DDPM under privacy constraints has important implications for deployment decisions. GANs and DDPMs demonstrate superior robustness when differential privacy noise is introduced during training, maintaining usable synthetic data quality across varying privacy budgets. VAEs degrade more rapidly, suggesting they may be unsuitable for applications requiring both strong privacy guarantees and high data utility. This distinction matters because organizations increasingly face pressure to generate synthetic datasets for development and testing while maintaining privacy compliance.

For industry practitioners, these findings inform model selection in healthcare, finance, and other sensitive domains where data scarcity and privacy constraints coexist. The research suggests that GAN and DDPM architectures deserve preference in these scenarios, though the specific choice depends on downstream task requirements. The framework itself provides valuable methodology for evaluating generative models in privacy-conscious settings, enabling more rigorous comparison than previous approaches.

• →GANs and DDPMs maintain higher fidelity and utility than VAEs when differential privacy is applied during training.
• →No single generative model optimizes fidelity, privacy, and utility simultaneously under data scarcity conditions.
• →Model selection for synthetic data generation requires multidimensional evaluation rather than single-metric optimization.
• →VAEs show rapid performance degradation as privacy constraints increase, limiting their use in privacy-sensitive applications.
• →The proposed evaluation framework enables more rigorous assessment of generative models for real-world deployment scenarios.