Disparate Impact in Differential Privacy from Gradient Misalignment

15 June 2022

Papers citing "Disparate Impact in Differential Privacy from Gradient Misalignment"

20 / 20 papers shown

Title
Conformal Prediction Sets Can Cause Disparate Impact Jesse C. Cresswell Bhargava Kumar Yi Sui Mouloud Belbahri FaML 84 1 0 17 Feb 2025
Differentially Private Prototypes for Imbalanced Transfer Learning Dariush Wahdany Matthew Jagielski Adam Dziedzic Franziska Boenisch 88 0 0 17 Feb 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients Clément Lalanne Jean-Michel Loubes David Rodríguez-Vítores FedML 46 0 0 03 Feb 2025
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight Tao Huang Qingyu Huang Xin Shi Jiayang Meng Guolong Zheng Xu Yang Xun Yi 31 0 0 05 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
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning Luca Corbucci Mikko A. Heikkilä David Solans Noguero Anna Monreale Nicolas Kourtellis FedML 52 3 0 21 Jul 2024
Differentially Private Fair Binary Classifications Hrad Ghoukasian S. Asoodeh FaML 40 1 0 23 Feb 2024
Regulation Games for Trustworthy Machine Learning Mohammad Yaghini Patty Liu Franziska Boenisch Nicolas Papernot FaML 23 2 0 05 Feb 2024
On the Impact of Output Perturbation on Fairness in Binary Linear Classification Vitalii Emelianov Michael Perrot FaML 35 0 0 05 Feb 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 47 2 0 07 Dec 2023
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning Kangkang Sun Xiaojin Zhang Xi Lin Gaolei Li Jing Wang Jianhua Li 45 4 0 30 Nov 2023
Fair Machine Unlearning: Data Removal while Mitigating Disparities Alexander X. Oesterling Jiaqi Ma Flavio du Pin Calmon Hima Lakkaraju FaML MU 31 19 0 27 Jul 2023
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off Huiqiang Chen Tianqing Zhu Tao Zhang Wanlei Zhou Philip S. Yu FedML 29 43 0 25 Jun 2023
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness Jiapeng Wu Atiyeh Ashari Ghomi David Glukhov Jesse C. Cresswell Franziska Boenisch Nicolas Papernot AAML 37 1 0 14 Jun 2023
Differentially Private Learning with Per-Sample Adaptive Clipping Tianyu Xia Shuheng Shen Su Yao Xinyi Fu Ke Xu Xiaolong Xu Xingbo Fu 30 16 0 01 Dec 2022
A Closer Look at the Calibration of Differentially Private Learners Hanlin Zhang Xuechen Li Prithviraj Sen Salim Roukos Tatsunori Hashimoto 16 3 0 15 Oct 2022
Pre-trained Perceptual Features Improve Differentially Private Image Generation Fredrik Harder Milad Jalali Asadabadi Danica J. Sutherland Mijung Park 36 28 0 25 May 2022
On the Convergence and Calibration of Deep Learning with Differential Privacy Zhiqi Bu Hua Wang Zongyu Dai Qi Long 33 27 0 15 Jun 2021
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 70 57 0 09 Feb 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 329 4,223 0 23 Aug 2019