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  3. 2009.06389
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Neither Private Nor Fair: Impact of Data Imbalance on Utility and
  Fairness in Differential Privacy

Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy

10 September 2020
Tom Farrand
Fatemehsadat Mireshghallah
Sahib Singh
Andrew Trask
    FedML
ArXivPDFHTML

Papers citing "Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy"

11 / 11 papers shown
Title
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
Soyeon Kim
Yuji Roh
Geon Heo
Steven Euijong Whang
39
0
0
03 Oct 2024
On the Fairness Impacts of Private Ensembles Models
On the Fairness Impacts of Private Ensembles Models
Cuong Tran
Ferdinando Fioretto
39
4
0
19 May 2023
Learning with Impartiality to Walk on the Pareto Frontier of Fairness,
  Privacy, and Utility
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility
Mohammad Yaghini
Patty Liu
Franziska Boenisch
Nicolas Papernot
FedML
FaML
17
8
0
17 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI
  models in medical imaging
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh
Alexander Ziller
Christiane Kuhl
Marcus R. Makowski
S. Nebelung
R. Braren
Daniel Rueckert
Daniel Truhn
Georgios Kaissis
MedIm
37
17
0
03 Feb 2023
VisCUIT: Visual Auditor for Bias in CNN Image Classifier
VisCUIT: Visual Auditor for Bias in CNN Image Classifier
Seongmin Lee
Zijie J. Wang
Judy Hoffman
Duen Horng Chau
24
11
0
12 Apr 2022
Exploring the Unfairness of DP-SGD Across Settings
Exploring the Unfairness of DP-SGD Across Settings
Frederik Noe
R. Herskind
Anders Søgaard
17
4
0
24 Feb 2022
Differential Privacy and Fairness in Decisions and Learning Tasks: A
  Survey
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
Ferdinando Fioretto
Cuong Tran
Pascal Van Hentenryck
Keyu Zhu
FaML
24
60
0
16 Feb 2022
Robin Hood and Matthew Effects: Differential Privacy Has Disparate
  Impact on Synthetic Data
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
Georgi Ganev
Bristena Oprisanu
Emiliano De Cristofaro
37
57
0
23 Sep 2021
A Fairness Analysis on Private Aggregation of Teacher Ensembles
A Fairness Analysis on Private Aggregation of Teacher Ensembles
Cuong Tran
M. H. Dinh
Kyle Beiter
Ferdinando Fioretto
21
12
0
17 Sep 2021
Privacy Regularization: Joint Privacy-Utility Optimization in Language
  Models
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah
Huseyin A. Inan
Marcello Hasegawa
Victor Rühle
Taylor Berg-Kirkpatrick
Robert Sim
16
39
0
12 Mar 2021
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
323
4,203
0
23 Aug 2019
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