arXiv:2505.22322v3 Announce Type: replace Abstract: Diffusion models have shown strong performance in generating high-quality tabular data, but they carry privacy risks by reproducing exact training samples. While prior work focuses on dataset-level augmentation to reduce memorization, little is known about which individual samples contribute most. We present the first data-centric study of memorization dynamics in tabular diffusion models. We quantify memorization for each real sample based on how many generated samples are flagged as replicas, using a relative distance ratio. Our empirical a
The rapid advancement and deployment of generative AI models, particularly diffusion models, necessitate a deeper understanding of their inherent risks, with memorization being a critical privacy concern gaining immediate attention.
Understanding and mitigating memorization in tabular diffusion models is crucial for their responsible adoption in sensitive data environments, impacting data privacy regulations, trusted AI development, and the utility of synthetic data.
This research provides a data-centric methodology to quantify memorization at the individual sample level, moving beyond dataset-level analysis and enabling more targeted interventions to improve data privacy.
- · Privacy-focused AI developers
- · Data privacy regulators
- · Organizations handling sensitive tabular data
- · Synthetic data providers
- · Models with high memorization rates
- · Developers neglecting data privacy in AI
- · Users vulnerable to data reconstruction attacks
Improved methods for identifying and mitigating privacy risks in generative AI models will emerge.
New standards and best practices for evaluating and certifying the privacy-preserving qualities of synthetic data will be developed.
The widespread adoption of privacy-enhanced generative AI could accelerate data sharing and collaborative AI development in regulated industries.
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