Privacy Distillation: Reducing Re-identification Risk of Multimodal
  Diffusion Models
Virginia Fernandez Pedro Sanchez Walter Hugo Lopez Pinaya Grzegorz Jacenków Sotirios A. Tsaftaris Jorge Cardoso
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 model is not leaking identifiable
information about a patient?''. Our solution consists of (1) training a first
diffusion model on real data (2) generating a synthetic dataset using this
model and filtering it to exclude images with a re-identifiability risk (3)
training a second diffusion model on the filtered synthetic data only. We
showcase that datasets sampled from models trained with privacy distillation
can effectively reduce re-identification risk whilst maintaining downstream
performance.