Differentially Private and Fair Classification via Calibrated Functional
Mechanism

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Differentially Private and Fair Classification via Calibrated Functional Mechanism

14 January 2020

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

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Title
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 111 0 0 03 Feb 2025
MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage Ying Song Balaji Palanisamy 251 1 0 28 Jan 2025
Understanding Fairness Surrogate Functions in Algorithmic Fairness Wei Yao Zhanke Zhou Zhicong Li Bo Han Yong Liu 64 5 0 17 Oct 2023
Fairly Private: Investigating The Fairness of Visual Privacy Preservation Algorithms Sophie Noiret Siddharth Ravi M. Kampel Francisco Flórez-Revuelta PICV 70 1 0 12 Jan 2023
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey Max Hort Zhenpeng Chen Jie M. Zhang Mark Harman Federica Sarro FaML AI4CE 105 176 0 14 Jul 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 103 56 0 17 Sep 2021
Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks Marlotte Pannekoek G. Spigler FedML 68 26 0 11 Feb 2021
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 195 113 0 07 Nov 2020
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning Jiahao Ding Jingyi Wang Guannan Liang J. Bi Miao Pan 48 12 0 11 Aug 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 76 130 0 05 Aug 2020