Deep Variational Privacy Funnel: General Modeling with Applications in
  Face Recognition
Behrooz Razeghi Parsa Rahimi Sébastien Marcel
In this study, we harness the information-theoretic Privacy Funnel (PF) model
to develop a method for privacy-preserving representation learning using an
end-to-end training framework. We rigorously address the trade-off between
obfuscation and utility. Both are quantified through the logarithmic loss, a
measure also recognized as self-information loss. This exploration deepens the
interplay between information-theoretic privacy and representation learning,
offering substantive insights into data protection mechanisms for both
discriminative and generative models. Importantly, we apply our model to
state-of-the-art face recognition systems. The model demonstrates adaptability
across diverse inputs, from raw facial images to both derived or refined
embeddings, and is competent in tasks such as classification, reconstruction,
and generation.