Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach

26 September 2020

Cuong Tran

Ferdinando Fioretto

Pascal Van Hentenryck

FedML

ArXiv PDF HTML

Papers citing "Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach"

20 / 20 papers shown

Title
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 44 0 0 03 Oct 2024
Near-Optimal Solutions of Constrained Learning Problems Juan Elenter Luiz F. O. Chamon Alejandro Ribeiro 26 5 0 18 Mar 2024
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach Kai Li Jingjing Zheng Xinnan Yuan W. Ni Ozgur B. Akan H. Vincent Poor AAML 32 15 0 30 Nov 2023
Resilient Constrained Learning Ignacio Hounie Alejandro Ribeiro Luiz F. O. Chamon 37 10 0 04 Jun 2023
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 43 4 0 19 May 2023
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility Mohammad Yaghini Patty Liu Franziska Boenisch Nicolas Papernot FedML FaML 46 8 0 17 Feb 2023
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks He Zhang Xingliang Yuan Shirui Pan 63 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
The intersection of machine learning with forecasting and optimisation: theory and applications M. Abolghasemi 34 2 0 24 Nov 2022
Fairness Increases Adversarial Vulnerability Cuong Tran Keyu Zhu Ferdinando Fioretto Pascal Van Hentenryck 36 6 0 21 Nov 2022
Differential Privacy has Bounded Impact on Fairness in Classification Paul Mangold Michaël Perrot A. Bellet Marc Tommasi 41 17 0 28 Oct 2022
Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova Atiyeh Ashari Ghomi Yaqiao Luo Jesse C. Cresswell 29 26 0 15 Jun 2022
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey Ferdinando Fioretto Cuong Tran Pascal Van Hentenryck Keyu Zhu FaML 37 61 0 16 Feb 2022
Equity and Privacy: More Than Just a Tradeoff David Pujol Ashwin Machanavajjhala 40 15 0 08 Nov 2021
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 46 56 0 23 Sep 2021
A Fairness Analysis on Private Aggregation of Teacher Ensembles Cuong Tran M. H. Dinh Kyle Beiter Ferdinando Fioretto 29 12 0 17 Sep 2021
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 53 0 17 Sep 2021
Federated Learning Meets Fairness and Differential Privacy P. Manisha Sankarshan Damle Sujit Gujar FedML 38 21 0 23 Aug 2021
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 38 110 0 07 Nov 2020
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods Ferdinando Fioretto Terrence W.K. Mak Pascal Van Hentenryck AI4CE 81 199 0 19 Sep 2019