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Found 4 papers, 0 papers with code
Three Variants of Differential Privacy: Lossless Conversion and Applications
In the first part, we develop a machinery for optimally relating approximate DP to RDP based on the joint range of two $f$-divergences that underlie the approximate DP and RDP.
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of R\'enyi differential privacy (RDP).
Theoretical Guarantees for Model Auditing with Finite Adversaries
Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data.
Generating Fair Universal Representations using Adversarial Models
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori.
