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arXiv · 2306.01322

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
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Abstract

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.

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Virginia Fernandez, Pedro Sanchez, Walter Hugo Lopez Pinaya, Grzegorz Jacenków, Sotirios A. Tsaftaris, Jorge Cardoso. 2023-06-02. Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models. https://arxiv.org/abs/2306.01322

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