PriSampler: Mitigating Property Inference of Diffusion Models. (arXiv:2306.05208v1 [cs.CR])

Diffusion models have been remarkably successful in data synthesis. Such
successes have also driven diffusion models to apply to sensitive data, such as
human face data, but this might bring about severe privacy concerns. In this
work, we systematically present the first privacy study about property
inference attacks against diffusion models, in which adversaries aim to extract
sensitive global properties of the training set from a diffusion model, such as
the proportion of the training data for certain sensitive properties. …

attacks data diffusion models human privacy privacy concerns sensitive data study work