arXiv:2606.07640v1 Announce Type: cross Abstract: This study investigates the trade-offs between fidelity, privacy, and utility in synthetic data generation under conditions of data scarcity and privacy sensitivity. We propose an evaluation framework that jointly assesses these three dimensions and apply it to three widely used generative models, VAE, GAN, and DDPM. The evaluation spans three image datasets, MNIST, OCTMNIST, and OrganAMNIST, encompassing both general-purpose and medical imaging domains. Notable differences arise between the three models in their behaviour when differential pri
This research is published as synthetic data generation becomes a critical component in AI development, particularly given increasing data privacy regulations and the computational demands of large models.
A strategic reader should care because the limitations of synthetic data under scarcity conditions directly impact the feasibility and reliability of AI applications in sensitive and data-poor domains, especially in fields like medical imaging.
This study clarifies that not all synthetic data generation methods perform equally well under data scarcity, introducing new considerations for model selection and evaluation in practical AI deployments.
- · Generative models with superior fidelity-utility trade-offs
- · Organizations with robust real-world data collection strategies
- · Researchers focused on privacy-preserving AI
- · AI projects relying solely on synthetic data in scarce domains
- · Generative models that overemphasize fidelity without utility
- · Sectors with inherent data scarcity challenges
AI developers will need to re-evaluate their synthetic data strategies, especially for sensitive areas like medical AI.
This could drive innovation in more robust synthetic data generation techniques optimized for scarcity or increase the demand for secure data-sharing frameworks.
Long-term, this research might influence regulatory bodies to set clearer standards for AI models trained partially or wholly on synthetic data.
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Read at arXiv cs.LG