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When we enforce differential privacy in machine learning, the utility-privacy trade-off is different w.r.t.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
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Building classifiers with independency constraints
T. Calders, F. Kamiran, and M. Pechenizkiy · 2009
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Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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Discrimination aware decision tree learning
F. Kamiran, T. Calders, and M. Pechenizkiy · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2011
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Fairness-aware learning through regularization approach
T. Kamishima, S. Akaho, and J. Sakuma · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
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Local privacy and statistical minimax rates
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
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Certifying and Removing Disparate Impact
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Discrimination- and privacy-aware patterns
Sara Hajian, Josep Domingo-Ferrer, Anna Monreale, Dino Pedreschi, and Fosca Giannotti · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Cited alongside, same era.
Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H. Chi · 2017
Cited alongside, same era.
Adaptive laplace mechanism: Differential privacy preservation in deep learning
NhatHai Phan, Xintao Wu, Han Hu, and Dejing Dou · 2017
Cited alongside, same era.
Controllable Invariance through Adversarial Feature Learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, and Graham Neubig · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi · 2017
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Bounding user contributions: A bias-variance trade-off in differential privacy
Kareem Amin, Alex Kulesza, Andres Muñoz Medina, and Sergei Vassilvitskii · 2019
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
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On the compatibility of privacy and fairness
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern · 2019
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Robust anomaly detection and backdoor attack detection via differential privacy
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Cited alongside, same era.
A causal framework for discovering and removing direct and indirect discrimination
Lu Zhang, Yongkai Wu, and Xintao Wu · 2017
Cited alongside, same era.
Model-agnostic private learning
Raef Bassily, Abhradeep Guha Thakurta, and Om Dipakbhai Thakkar · 2018
Cited alongside, same era.
Privacy for all: Ensuring fair and equitable privacy protections
Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael J. Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
Adaptive sensitive reweighting to mitigate bias in fairness-aware classification
Emmanouil Krasanakis, Eleftherios Spyromitros Xioufis, Symeon Papadopoulos, and Yiannis Kompatsiaris · 2018
Cited alongside, same era.
Concentrated differentially private gradient descent with adaptive per-iteration privacy budget
Jaewoo Lee and Daniel Kifer · 2018
Cited alongside, same era.
Min Du, Ruoxi Jia, and Dawn Song · 2019
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Differentially private fair learning
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman · 2019
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Privacy enhanced multimodal neural representations for emotion recognition
Mimansa Jaiswal and Emily Mower Provost · 2019
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Adaclip: Adaptive clipping for private SGD
Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, and Sanjiv Kumar · 2019
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Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H. Brendan McMahan · 2019
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Achieving differential privacy and fairness in logistic regression
Depeng Xu, Shuhan Yuan, and Xintao Wu · 2019
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Differentially private and fair classification via calibrated functional mechanism
Jiahao Ding, Xinyue Zhang, Xiaohuan Li, Junyi Wang, Rong Yu, and Miao Pan · 2020
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