Study Compares Trade-offs Between Privacy, Fidelity, and Utility in Synthetic Image Generation
A new preprint proposes a framework for evaluating synthetic image generation models across fidelity, privacy, and utility, finding significant trade-offs under data scarcity. Tested on VAE, GAN, and DDPM across general and medical imaging datasets, the study found that GAN and DDPM better withstand differential privacy constraints than VAE. The findings underscore that no single generative model excels across all three dimensions simultaneously, particularly when privacy mechanisms are applied.
Researchers have published a preprint on arXiv introducing an evaluation framework that jointly measures fidelity, privacy, and utility in synthetic data generation under conditions of data scarcity and privacy sensitivity. The study benchmarks three widely used generative models — Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Denoising Diffusion Probabilistic Models (DDPM) — across three image datasets: MNIST, OCTMNIST, and OrganAMNIST, the latter two representing medical imaging contexts. When differential privacy mechanisms were introduced during training, GAN and DDPM demonstrated greater robustness, maintaining higher fidelity and downstream utility across varying noise levels. VAE, by contrast, degraded more rapidly as privacy constraints increased. The authors argue that multidimensional evaluation is essential, as model behavior shifts substantially once privacy techniques are applied, and no single model offers a free lunch across all three criteria.
What's missing
As a preprint, this work has not yet undergone peer review. The study is limited to image data and three specific datasets; generalizability to other data modalities or domains is untested. The range of epsilon values used for differential privacy and the specific downstream tasks used to measure utility are not detailed in the abstract, leaving open questions about how sensitive the conclusions are to those choices. The study does not address computational cost trade-offs between models under privacy constraints.
What different sources said
- arXiv cs.AICenter
No Free Lunch for Synthetic Images under Data Scarcity Conditions
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