DPCP delivers end-to-end differentially private conformal prediction sets that are tighter than split-based private methods under the same privacy budget while maintaining coverage under regularity conditions.
R \'e nyi differential privacy
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Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
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Differentially Private Conformal Prediction
DPCP delivers end-to-end differentially private conformal prediction sets that are tighter than split-based private methods under the same privacy budget while maintaining coverage under regularity conditions.
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Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.