Differentially Private and Fair Classification via Calibrated Functional Mechanism

14 January 2020

Papers citing "Differentially Private and Fair Classification via Calibrated Functional Mechanism"

9 / 9 papers shown

Title
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 48 0 0 03 Feb 2025
MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage Ying Song Balaji Palanisamy 85 0 0 28 Jan 2025
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks He Zhang Xingliang Yuan Shirui Pan 53 11 0 30 Jan 2023
Fairly Private: Investigating The Fairness of Visual Privacy Preservation Algorithms Sophie Noiret Siddharth Ravi M. Kampel Francisco Flórez-Revuelta PICV 14 1 0 12 Jan 2023
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey Ferdinando Fioretto Cuong Tran Pascal Van Hentenryck Keyu Zhu FaML 32 60 0 16 Feb 2022
Enforcing fairness in private federated learning via the modified method of differential multipliers Borja Rodríguez Gálvez Filip Granqvist Rogier van Dalen M. Seigel FedML 48 52 0 17 Sep 2021
Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks Marlotte Pannekoek G. Spigler FedML 32 26 0 11 Feb 2021
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 38 110 0 07 Nov 2020
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 38 125 0 05 Aug 2020