Evaluating the Fairness Impact of Differentially Private Synthetic Data

9 May 2022

Papers citing "Evaluating the Fairness Impact of Differentially Private Synthetic Data"

7 / 7 papers shown

Title
SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI S. Giddens F. Liu 32 0 0 14 Nov 2024
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization Rafael Mendoza Isabella Cruz Richard Liu Aarav Deshmukh David Williams Jesscia Peng Rohan Iyer 29 1 0 25 Sep 2024
Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data Mayana Pereira Meghana Kshirsagar Soumendu Sundar Mukherjee Rahul Dodhia J. L. Ferres Rafael de Sousa SyDa 42 11 0 30 Oct 2023
A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics Tânia Carvalho Nuno Moniz Luís Antunes 30 2 0 27 Jun 2023
Diffusion Models: A Comprehensive Survey of Methods and Applications Ling Yang Zhilong Zhang Yingxia Shao Shenda Hong Runsheng Xu Yue Zhao Wentao Zhang Bin Cui Ming-Hsuan Yang DiffM MedIm 226 1,314 0 02 Sep 2022
The Optimal Mechanism in Differential Privacy: Multidimensional Setting Quan Geng Pramod Viswanath 89 123 0 02 Dec 2013