no code implementations • 14 Aug 2020 • Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar
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.
no code implementations • 16 Jan 2020 • Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar
We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of R\'enyi differential privacy (RDP).
no code implementations • 8 Nov 2019 • Mario Diaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar
Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data.
no code implementations • 27 Sep 2019 • Peter Kairouz, Jiachun Liao, Chong Huang, Maunil Vyas, Monica Welfert, Lalitha Sankar
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.