Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

23 September 2021

Papers citing "Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data"

26 / 26 papers shown

Title
Conformal Prediction Sets Can Cause Disparate Impact Jesse C. Cresswell Bhargava Kumar Yi Sui Mouloud Belbahri FaML 306 1 0 17 Feb 2025
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 58 0 0 03 Oct 2024
A Fairness Analysis on Private Aggregation of Teacher Ensembles Cuong Tran M. H. Dinh Kyle Beiter Ferdinando Fioretto 45 12 0 17 Sep 2021
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data Ryan McKenna G. Miklau Daniel Sheldon SyDa 26 120 0 11 Aug 2021
An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises Mayana Pereira Meghana Kshirsagar Soumendu Sundar Mukherjee Rahul Dodhia J. L. Ferres 44 9 0 15 Jun 2021
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 44 131 0 04 Feb 2021
PrivSyn: Differentially Private Data Synthesis Zhikun Zhang Tianhao Wang Ninghui Li Jean Honorio Michael Backes Shibo He Jiming Chen Yang Zhang SyDa 28 64 0 30 Dec 2020
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings Vinith Suriyakumar Nicolas Papernot Anna Goldenberg Marzyeh Ghassemi OOD 45 67 0 13 Oct 2020
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach Cuong Tran Ferdinando Fioretto Pascal Van Hentenryck FedML 46 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 35 93 0 10 Sep 2020
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning U. Tantipongpipat Chris Waites Digvijay Boob Amaresh Ankit Siva Rachel Cummings SyDa 50 31 0 06 Dec 2019
Diffprivlib: The IBM Differential Privacy Library N. Holohan S. Braghin Pól Mac Aonghusa Killian Levacher SyDa 47 129 0 04 Jul 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 113 489 0 12 Jun 2019
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 81 474 0 28 May 2019
Detecting Overfitting of Deep Generative Networks via Latent Recovery Ryan Webster Julien Rabin Loïc Simon F. Jurie GAN 33 99 0 09 Jan 2019
Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data Lorenzo Frigerio Anderson Santana de Oliveira L. Gomez Patrick Duverger SyDa AI4TS 48 110 0 08 Jan 2019
Differentially Private Fair Learning Matthew Jagielski Michael Kearns Jieming Mao Alina Oprea Aaron Roth Saeed Sharifi-Malvajerdi Jonathan R. Ullman FaML FedML 80 149 0 06 Dec 2018
Scalable Private Learning with PATE Nicolas Papernot Shuang Song Ilya Mironov A. Raghunathan Kunal Talwar Ulfar Erlingsson 86 608 0 24 Feb 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos D. Song 111 1,128 0 22 Feb 2018
Differentially Private Generative Adversarial Network Liyang Xie Kaixiang Lin Shu Wang Fei Wang Jiayu Zhou SyDa 66 495 0 19 Feb 2018
Differentially Private Mixture of Generative Neural Networks G. Ács Luca Melis C. Castelluccia Emiliano De Cristofaro SyDa 36 121 0 13 Sep 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 203 4,075 0 18 Oct 2016
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 55 1,012 0 18 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 124 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 170 6,069 0 01 Jul 2016
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 93 1,482 0 01 Dec 2009