PreFair: Privately Generating Justifiably Fair Synthetic Data

20 December 2022

Papers citing "PreFair: Privately Generating Justifiably Fair Synthetic Data"

5 / 5 papers shown

Title
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
Privacy-Enhanced Database Synthesis for Benchmark Publishing (Technical Report) Yongrui Zhong Yunqing Ge Jianbin Qin Yongrui Zhong Bo Tang Yu-Xuan Qiu Rui Mao Ye Yuan Makoto Onizuka Chuan Xiao 34 0 0 02 May 2024
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
Leveraging Public Data for Practical Private Query Release Terrance Liu G. Vietri Thomas Steinke Jonathan R. Ullman Zhiwei Steven Wu 155 58 0 17 Feb 2021
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016