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Membership inference (MI) attacks exploit the fact that machine learning algorithms sometimes leak information about their training data through the learned model.
The MNIST database of handwritten digits
Yann LeCun, Corrina Cortes, and Christopher Burges · 1998
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Differential privacy
Cynthia Dwork · 2006
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Gaussian classifiers
Kevin P. Murphy · 2007
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The cost of privacy: destruction of data-mining utility in anonymized data publishing
Justin Brickell and Vitaly Shmatikov · 2008
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V. Pearson, Dietrich A. Stephan, Stanley F. Nelson, and David W. Craig · 2008
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Learning in a large function space: Privacy-preserving mechanisms for SVM learning
Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft · 2009
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Genomic privacy and limits of individual detection in a pool
Sriram Sankararaman, Guillaume Obozinski, Michael I Jordan, and Eran Halperin · 2009
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Learning your identity and disease from research papers: information leaks in genome wide association studies
Rui Wang, Yong Fuga Li, XiaoFeng Wang, Haixu Tang, and Xiaoyong Zhou · 2009
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Personal privacy vs population privacy: Learning to attack anonymization
Graham Cormode · 2011
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A systematic review of re-identification attacks on health data
Khaled El Emam, Elizabeth Jonker, Luk Arbuckle, and Bradley Malin · 2011
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Unsupervised feature learning and deep learning: A review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2012
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Machine Learning: A Probabilistic Perspective
Kevin P. Murphy · 2012
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Identifying personal genomes by surname inference
Melissa Gymrek, Amy L. McGuire, David Golan, Eran Halperin, and Yaniv Erlich · 2013
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Membership privacy: A unifying framework for privacy definitions
Ninghui Li, Wahbeh Qardaji, Dong Su, Yi Wu, and Weining Yang · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Cited alongside, same era.
Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Cited alongside, same era.
Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Giuseppe Ateniese, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici · 2015
Keras: Deep learning library for Theano and TensorFlow
Francois Chollet · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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LOGAN: evaluating privacy leakage of generative models using generative adversarial networks
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2017
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Deep models under the GAN: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
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Towards measuring membership privacy
Yunhui Long, Vincent Bindschaedler, and Carl A. Gunter · 2017
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Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Cited alongside, same era.
To drop or not to drop: Robustness, consistency and differential privacy properties of dropout
Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta, and Oliver Williams · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Estimation of treatment effects from combined data: Identification versus data security
Tatiana Komarova, Denis Nekipelov, and Evgeny Yakovlev · 2015
Cited alongside, same era.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
Privacy risks from genomic data-sharing beacons
Suyash S. Shringarpure and Carlos D. Bustamante · 2015
Cited alongside, same era.
Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences
X. Wu, M. Fredrikson, W. Wu, S. Jha, and J. F. Naughton · 2015
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Privacy loss in apple’s implementation of differential privacy on macos 10.12
Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, and Xiaofeng Wang · 2017
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The unintended consequences of overfitting: Training data inference attacks
Samuel Yeom, Matt Fredrikson, and Somesh Jha · 2017
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Influence-directed explanations for deep convolutional networks
Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, and Linyi Li · 2018
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Understanding membership inferences on well-generalized learning models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A. Gunter, and Kai Chen · 2018
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A general approach to adding differential privacy to iterative training procedures
H. Brendan McMahan and Galen Andrew · 2018
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Inference attacks against collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2018
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Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
Closest in time.
Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, and Michael Backes · 2019
Closest in time.