10 citations · 33 across the 12 of their papers we have counts for
3 papers · 1 filter
Theoretical Guarantees for Model Auditing with Finite Adversaries
Mario Diaz, Peter Kairouz, Jiachun Liao +1
Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data. Yet, in practice, even models learned with privacy guarantees…
Generating Fair Universal Representations using Adversarial Models
Peter Kairouz, Jiachun Liao, Chong Huang +3
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. Our f…
Robustness of Maximal -Leakage to Side Information
Jiachun Liao, Lalitha Sankar, Oliver Kosut +1
Maximal -leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter dete…