Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition
This addresses privacy protection in face recognition systems, but it is incremental as it builds on existing Privacy Funnel concepts.
The study tackled the trade-off between privacy and utility in representation learning by developing a method based on the information-theoretic Privacy Funnel model, applying it to face recognition systems and demonstrating adaptability across various inputs and tasks.
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.