DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

22 June 2021

Rakshit Naidu

Sasikanth Kotti

Fatemehsadat Mireshghallah

Papers citing "DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?"

11 / 11 papers shown

Title
Privacy at a Price: Exploring its Dual Impact on AI Fairness Mengmeng Yang Ming Ding Youyang Qu Wei Ni David B. Smith Thierry Rakotoarivelo 30 1 0 15 Apr 2024
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 41 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 41 8 0 17 Feb 2023
Differential Privacy has Bounded Impact on Fairness in Classification Paul Mangold Michaël Perrot A. Bellet Marc Tommasi 31 17 0 28 Oct 2022
Pruning has a disparate impact on model accuracy Cuong Tran Ferdinando Fioretto Jung-Eun Kim Rakshit Naidu 41 38 0 26 May 2022
Exploring the Unfairness of DP-SGD Across Settings Frederik Noe R. Herskind Anders Søgaard 27 4 0 24 Feb 2022
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
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 Cuong Tran M. H. Dinh Kyle Beiter Ferdinando Fioretto 21 12 0 17 Sep 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 194 0 26 Feb 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 335 4,223 0 23 Aug 2019