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A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not.
On the learnability of discrete distributions
Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, and Linda Sellie · 1994
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Ron Kohavi · 1996
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Consumer credit-risk models via machine-learning algorithms
Amir E Khandani, Adlar J Kim, and Andrew W Lo · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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Differential Privacy
Cynthia Dwork · 2011
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Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 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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Experimental design and analysis
Howard J Seltman · 2012
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A probabilistic theory of pattern recognition , volume 31
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
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On the (im) possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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To predict and serve?
Kristian Lum and William Isaac · 2016
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Predicting the future—big data, machine learning, and clinical medicine
Ziad Obermeyer and Ezekiel J Emanuel · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2018
Cited alongside, same era.
The frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 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.
Does mitigating ML’s impact disparity require treatment disparity?
Zachary C. Lipton, Julian McAuley, and Alexandra Chouldechova · 2018
Cited alongside, same era.
Inherent tradeoffs in learning fair representations
Han Zhao and Geoffrey J. Gordon · 2019
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Fairlearn: A toolkit for assessing and improving fairness in AI
Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker · 2020
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Konstantinos Chatzikokolakis, Giovanni Cherubin, Catuscia Palamidessi, and Carmela Troncoso · 2020
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Modelling and quantifying membership information leakage in machine learning
Farhad Farokhi and Mohamed Ali Kaafar · 2020
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2020 ends a decade of 62 new data privacy laws
Graham Greenleaf and Bertil Cottier · 2020
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Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
Algorithms that remember: model inversion attacks and data protection law
Michael Veale, Reuben Binns, and Lilian Edwards · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
The Localisation Gambit: Unpacking Policy Measures for Sovereign Control of Data in India
Arindrajit Basu, Elonnai Hickok, and Aditya Singh Chawala · 2019
Cited alongside, same era.
F-BLEAU: Fast black-box leakage estimation
Giovanni Cherubin, Konstantinos Chatzikokolakis, and Catuscia Palamidessi · 2019
Cited alongside, same era.
Diffprivlib: The IBM differential privacy library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
Cited alongside, same era.
Thomas Humphries, Matthew Rafuse, Lindsey Tulloch, Simon Oya, Ian Goldberg, Urs Hengartner, and Florian Kerschbaum · 2020
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Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Klas Leino and Matt Fredrikson · 2020
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A pragmatic approach to membership inferences on machine learning models
Yunhui Long, Lei Wang, Diyue Bu, Vincent Bindschaedler, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen · 2020
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Distributional generalization: A new kind of generalization
Preetum Nakkiran and Yamini Bansal · 2020
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Fair decision making using privacy-protected data
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2020
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On the privacy risks of algorithmic fairness
Hongyan Chang and Reza Shokri · 2021
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Revisiting membership inference under realistic assumptions
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2021
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Membership inference attacks and defenses in classification models
Jiacheng Li, Ninghui Li, and Bruno Ribeiro · 2021
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Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2021
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