Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models. (arXiv:2306.01322v1 [cs.LG])

Knowledge distillation in neural networks refers to compressing a large model
or dataset into a smaller version of itself. We introduce Privacy Distillation,
a framework that allows a text-to-image generative model to teach another model
without exposing it to identifiable data. Here, we are interested in the
privacy issue faced by a data provider who wishes to share their data via a
multimodal generative model. A question that immediately arises is ``How can a
data provider ensure that the generative …

data diffusion models exposing framework generative identification issue knowledge large networks neural networks privacy risk teach text version