5 citations · 6 across the 3 of their papers we have counts for
4 papers
Fairness Increases Adversarial Vulnerability
Cuong Tran, Keyu Zhu, Ferdinando Fioretto +1
The remarkable performance of deep learning models and their applications in consequential domains (e.g., facial recognition) introduces important challenges at the intersection of…
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
Cuong Tran, Keyu Zhu, Ferdinando Fioretto +1
A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure no…
Post-processing of Differentially Private Data: A Fairness Perspective
Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affectin…
Differential Privacy for Stackelberg Games
Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck
This paper introduces a differentially private (DP) mechanism to protect the information exchanged during the coordination of sequential and interdependent markets. This coordinati…