PFGuard: A Generative Framework with Privacy and Fairness Safeguards

3 October 2024

Papers citing "PFGuard: A Generative Framework with Privacy and Fairness Safeguards"

50 / 52 papers shown

Title
Training Unbiased Diffusion Models From Biased Dataset Yeongmin Kim Byeonghu Na Minsang Park Joonho Jang Dongjun Kim Wanmo Kang Il-Chul Moon 40 21 0 02 Mar 2024
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy Lucas Rosenblatt Julia Stoyanovich Christopher Musco 35 2 0 18 Dec 2023
FFPDG: Fast, Fair and Private Data Generation Weijie Xu Jinjin Zhao Francis Iannacci Bo Wang 50 11 0 30 Jun 2023
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation Yilin Yang Kamil Adamczewski Danica J. Sutherland Xiaoxiao Li Mijung Park 55 14 0 03 Mar 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images Sahra Ghalebikesabi Leonard Berrada Sven Gowal Ira Ktena Robert Stanforth Jamie Hayes Soham De Samuel L. Smith Olivia Wiles Borja Balle DiffM 62 69 0 27 Feb 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 57 8 0 17 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 71 14 0 06 Feb 2023
PreFair: Privately Generating Justifiably Fair Synthetic Data David Pujol Amir Gilad Ashwin Machanavajjhala 43 7 0 20 Dec 2022
Fair Generative Models via Transfer Learning Christopher T. H. Teo Milad Abdollahzadeh Ngai-Man Cheung 60 24 0 02 Dec 2022
Differentially Private Diffusion Models Tim Dockhorn Tianshi Cao Arash Vahdat Karsten Kreis DiffM 68 93 0 18 Oct 2022
Stochastic Differentially Private and Fair Learning Andrew Lowy Devansh Gupta Meisam Razaviyayn FaML FedML 33 14 0 17 Oct 2022
Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova Atiyeh Ashari Ghomi Yaqiao Luo Jesse C. Cresswell 44 27 0 15 Jun 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu Wang Sheng Zha George Karypis 82 70 0 14 Jun 2022
Evaluating the Fairness Impact of Differentially Private Synthetic Data Blake Bullwinkel Kristen Grabarz Lily Ke Scarlett Gong Christy Tanner Joshua Allen SyDa 33 9 0 09 May 2022
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles Cuong Tran Keyu Zhu Ferdinando Fioretto Pascal Van Hentenryck 47 12 0 11 Apr 2022
What You See is What You Get: Principled Deep Learning via Distributional Generalization B. Kulynych Yao-Yuan Yang Yaodong Yu Jarosław Błasiok Preetum Nakkiran OOD 44 10 0 07 Apr 2022
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers Dominik Zietlow Michael Lohaus Guha Balakrishnan Matthäus Kleindessner Francesco Locatello Bernhard Schölkopf Chris Russell FaML 32 73 0 09 Mar 2022
Adversarial Attacks Against Deep Generative Models on Data: A Survey Hui Sun Tianqing Zhu Zhiqiu Zhang Dawei Jin Wanlei Zhou AAML 91 42 0 01 Dec 2021
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence Tianshi Cao Alex Bie Arash Vahdat Sanja Fidler Karsten Kreis SyDa DiffM 63 71 0 01 Nov 2021
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining Ahmed Imtiaz Humayun Randall Balestriero Richard Baraniuk OOD 78 29 0 15 Oct 2021
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 62 56 0 23 Sep 2021
Hermite Polynomial Features for Private Data Generation Margarita Vinaroz Mohammad-Amin Charusaie Frederik Harder Kamil Adamczewski Mijung Park 45 26 0 09 Jun 2021
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning Seng Pei Liew Tsubasa Takahashi Michihiko Ueno FedML 45 30 0 08 Jun 2021
Differentially Empirical Risk Minimization under the Fairness Lens Cuong Tran My H. Dinh Ferdinando Fioretto 19 46 0 04 Jun 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation Wei Ping Fan Wu Yunhui Long Luka Rimanic Ce Zhang Yue Liu FedML 71 64 0 20 Mar 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 277 493 0 31 Dec 2020
Improving the Fairness of Deep Generative Models without Retraining Shuhan Tan Yujun Shen Bolei Zhou 206 59 0 09 Dec 2020
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach Cuong Tran Ferdinando Fioretto Pascal Van Hentenryck FedML 49 78 0 26 Sep 2020
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy Tom Farrand Fatemehsadat Mireshghallah Sahib Singh Andrew Trask FedML 37 93 0 10 Sep 2020
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model Shun Takagi Tsubasa Takahashi Yang Cao Masatoshi Yoshikawa SyDa 46 39 0 22 Jun 2020
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators Dingfan Chen Tribhuvanesh Orekondy Mario Fritz SyDa 39 183 0 15 Jun 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 546 41,106 0 28 May 2020
Fair Learning with Private Demographic Data Hussein Mozannar Mesrob I. Ohannessian Nathan Srebro 47 75 0 26 Feb 2020
DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation Frederik Harder Kamil Adamczewski Mijung Park SyDa 92 101 0 26 Feb 2020
DP-CGAN: Differentially Private Synthetic Data and Label Generation Reihaneh Torkzadehmahani Peter Kairouz B. Paten SyDa 51 236 0 27 Jan 2020
Fair Generative Modeling via Weak Supervision Kristy Choi Aditya Grover Trisha Singh Rui Shu Stefano Ermon 74 134 0 26 Oct 2019
Mining GOLD Samples for Conditional GANs Sangwoo Mo Chiheon Kim Sungwoong Kim Minsu Cho Jinwoo Shin GAN AI4CE 52 20 0 21 Oct 2019
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators Yunhui Long Wei Ping Zhuolin Yang B. Kailkhura Aston Zhang C.A. Gunter Yue Liu 92 72 0 21 Jun 2019
Reconstruction and Membership Inference Attacks against Generative Models Benjamin Hilprecht Martin Härterich Daniel Bernau AAML MIACV 54 186 0 07 Jun 2019
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 89 474 0 28 May 2019
Differentially Private Fair Learning Matthew Jagielski Michael Kearns Jieming Mao Alina Oprea Aaron Roth Saeed Sharifi-Malvajerdi Jonathan R. Ullman FaML FedML 86 149 0 06 Dec 2018
Bias and Generalization in Deep Generative Models: An Empirical Study Shengjia Zhao Hongyu Ren Arianna Yuan Jiaming Song Noah D. Goodman Stefano Ermon AI4CE 42 137 0 08 Nov 2018
FairGAN: Fairness-aware Generative Adversarial Networks Depeng Xu Shuhan Yuan Lu Zhang Xintao Wu GAN 100 311 0 28 May 2018
Scalable Private Learning with PATE Nicolas Papernot Shuang Song Ilya Mironov A. Raghunathan Kunal Talwar Ulfar Erlingsson 86 608 0 24 Feb 2018
Differentially Private Generative Adversarial Network Liyang Xie Kaixiang Lin Shu Wang Fei Wang Jiayu Zhou SyDa 76 495 0 19 Feb 2018
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 211 8,807 0 25 Aug 2017
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 66 1,012 0 18 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 143 4,276 0 07 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 175 6,069 0 01 Jul 2016
Fairness in Learning: Classic and Contextual Bandits Matthew Joseph Michael Kearns Jamie Morgenstern Aaron Roth FaML 50 473 0 23 May 2016