Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity

3 November 2022

Papers citing "Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity"

11 / 11 papers shown

Title
Training Set Reconstruction from Differentially Private Forests: How Effective is DP? Alice Gorgé Julien Ferry Sébastien Gambs Thibaut Vidal 62 0 0 07 Feb 2025
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning Fengyu Gao Ruida Zhou T. Wang Cong Shen Jing Yang 29 2 0 15 Oct 2024
A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition Faisal Hamman Sanghamitra Dutta 31 2 0 07 Jun 2024
Trained Random Forests Completely Reveal your Dataset Julien Ferry Ricardo Fukasawa Timothée Pascal Thibaut Vidal AAML 21 6 0 29 Feb 2024
FairProof : Confidential and Certifiable Fairness for Neural Networks Chhavi Yadav A. Chowdhury Dan Boneh Kamalika Chaudhuri MLAU 33 5 0 19 Feb 2024
Probabilistic Dataset Reconstruction from Interpretable Models Julien Ferry Ulrich Aivodji Sébastien Gambs Marie-José Huguet Mohamed Siala 13 5 0 29 Aug 2023
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition Faisal Hamman Sanghamitra Dutta FedML 17 6 0 21 Jul 2023
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning Marc Juárez Aleksandra Korolova FedML 28 9 0 24 Jun 2022
CryptoCredit: Securely Training Fair Models Leo de Castro Jiahao Chen Antigoni Polychroniadou 19 3 0 09 Oct 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,143 0 23 Aug 2019
Privacy Against Statistical Inference Flavio du Pin Calmon N. Fawaz FedML 83 345 0 08 Oct 2012