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This paper surveys recent work in the intersection of differential privacy (DP) and fairness.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
Boosting the accuracy of differentially private histograms through consistency
Michael Hay, Vibhor Rastogi, Gerome Miklau, and Dan Suciu · 2010
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Functional mechanism: Regression analysis under differential privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2013
Earlier work this paper cites.
Dpt: Differentially private trajectory synthesis using hierarchical reference systems
Xi He, Graham Cormode, Ashwin Machanavajjhala, Cecilia M. Procopiuc, and Divesh Srivastava · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Learning with privacy at scale
Apple Differential Privacy Team · 2017
Earlier work this paper cites.
Efficient private ERM for smooth objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou, and Liwei Wang · 2017
Earlier work this paper cites.
The us census bureau adopts differential privacy
John M Abowd · 2018
Earlier work this paper cites.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Earlier work this paper cites.
Constrained-based differential privacy for private mobility
Ferdinando Fioretto, Chansoo Lee, and Pascal Van Hentenryck · 2018
Earlier work this paper cites.
Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
Earlier work this paper cites.
Hessian-based analysis of large batch training and robustness to adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney · 2018
Earlier work this paper cites.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
On the compatibility of privacy and fairness
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern · 2019
Cited alongside, same era.
Differentially private fair learning
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman · 2019
Cited alongside, same era.
Differentially private empirical risk minimization with non-convex loss functions
Di Wang, Changyou Chen, and Jinhui Xu · 2019
Cited alongside, same era.
Achieving differential privacy and fairness in logistic regression
Depeng Xu, Shuhan Yuan, and Xintao Wu · 2019
Cited alongside, same era.
Inherent tradeoffs in learning fair representations
Han Zhao and Geoff Gordon · 2019
Cited alongside, same era.
Fair decision making using privacy-protected data
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2020
Later among the works it cites.
Differential privacy of hierarchical census data: An optimization approach
Ferdinando Fioretto, Pascal Van Hentenryck, and Keyu Zhu · 2021
Later among the works it cites.
Improving fairness and privacy in selection problems
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan, and Somayeh Sojoudi · 2021
Later among the works it cites.
Antipodes of label differential privacy: Pate and alibi
Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, and Florian Tramer · 2021
Later among the works it cites.
Federated learning meets fairness and differential privacy
Manisha Padala, Sankarshan Damle, and Sujit Gujar · 2021
Later among the works it cites.
Chasing your long tails: Differentially private prediction in health care settings
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Mitigating bias in federated learning
Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, and Heiko Ludwig · 2020
Cited alongside, same era.
Google covid-19 community mobility reports: Anonymization process description (version 1.1)
Ahmet Aktay, Shailesh Bavadekar, Gwen Cossoul, John Davis, Damien Desfontaines, Alex Fabrikant, Evgeniy Gabrilovich, Krishna Gadepalli, Bryant Gipson, Miguel Guevara, Chaitanya Kamath, Mansi Kansal, Ali Lange, Chinmoy Mandayam, Andrew Oplinger, Christopher Pluntke, Thomas Roessler, Arran Schlosberg, Tomer Shekel, Swapnil Vispute, Mia Vu, Gregory Wellenius, Brian Williams, and Royce J Wilson · 2020
Cited alongside, same era.
Differentially private and fair classification via calibrated functional mechanism
Jiahao Ding, Xinyue Zhang, Xiaohuan Li, Junyi Wang, Rong Yu, and Miao Pan · 2020
Cited alongside, same era.
Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy
Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, and Andrew Trask · 2020
Cited alongside, same era.
Lagrangian duality for constrained deep learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak, Cuong Tran, Federico Baldo, and Michele Lombardi · 2020
Cited alongside, same era.
Differential privacy for power grid obfuscation
Ferdinando Fioretto, Terrence W.K. Mak, and Pascal Van Hentenryck · 2020
Cited alongside, same era.
Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, and Marzyeh Ghassemi · 2021
Later among the works it cites.
Differentially private empirical risk minimization under the fairness lens
Cuong Tran, My Dinh, and Ferdinando Fioretto · 2021
Later among the works it cites.
A fairness analysis on private aggregation of teacher ensembles
Cuong Tran, My H Dinh, Kyle Beiter, and Ferdinando Fioretto · 2021
Later among the works it cites.
Differentially private and fair deep learning: A lagrangian dual approach
Cuong Tran, Ferdinando Fioretto, and Pascal Van Hentenryck · 2021
Later among the works it cites.
Decision making with differential privacy under a fairness lens
Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, and Zhiyan Yao · 2021
Later among the works it cites.
Dp-sgd vs pate: Which has less disparate impact on model accuracy?
Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, and Andrew Trask · 2021
Later among the works it cites.
Removing disparate impact on model accuracy in differentially private stochastic gradient descent
Depeng Xu, Wei Du, and Xintao Wu · 2021
Later among the works it cites.
Balancing learning model privacy, fairness, and accuracy with early stopping criteria
Tao Zhang, Tianqing Zhu, Kun Gao, Wanlei Zhou, and S Yu Philip · 2021
Later among the works it cites.
Bias and variance of post-processing in differential privacy
Keyu Zhu, Pascal Van Hentenryck, and Ferdinando Fioretto · 2021
Later among the works it cites.
Post-processing of differentially private data: A fairness perspective
Keyu Zhu, Ferdinando Fioretto, and Pascal Van Hentenryck · 2022
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