Study Compares Trade-offs Between Privacy, Fidelity, and Utility in Synthetic Image Generation
Researchers evaluated how three generative models (VAE, GAN, DDPM) balance fidelity, privacy, and utility when generating synthetic images under data scarcity conditions. The study found that GANs and DDPMs maintain performance better than VAEs when differential privacy is applied during training. The findings suggest that choosing the right generative model depends on specific privacy and utility requirements.
A new study published on arXiv examines the performance of three widely-used generative models—Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Denoising Diffusion Probabilistic Models (DDPM)—when generating synthetic images under conditions of limited data and privacy constraints. The researchers developed an evaluation framework that jointly assesses fidelity (image quality), privacy protection, and downstream utility across three datasets: MNIST, OCTMNIST, and OrganAMNIST, covering both general-purpose and medical imaging domains. A key finding is that GAN and DDPM models demonstrate greater robustness when differential privacy mechanisms are introduced, maintaining higher fidelity and utility across varying noise levels, while VAE performance degrades more rapidly as privacy constraints tighten. The study emphasizes that generative models exhibit significantly different behaviors when privacy techniques are applied, suggesting that practitioners cannot assume one model will universally outperform others. These results highlight the importance of multidimensional evaluation when selecting generative models for applications requiring both privacy protection and high-quality synthetic data.
What's missing
The study's own limitations and scope constraints are not detailed in the abstract provided, such as computational costs of the models, scalability to larger datasets, or applicability beyond image generation tasks.
What different sources said
- arXiv cs.AICenter
No Free Lunch for Synthetic Images under Data Scarcity Conditions
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