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Deep neural networks have been shown to be vulnerable to membership inference attacks wherein the attacker aims to detect whether specific input data were used to train the model.
Where is the information in a deep neural network?
Achille, A.; Paolini, G.; and Soatto, S. 2019 · 1905
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Transmission of information: A statistical theory of communications
Fano, R. M. 1961 · 1961
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A new statistic and its power to infer membership in a genome-wide association study using genotype frequencies
Jacobs, K. B.; Yeager, M.; Wacholder, S.; Craig, D.; Kraft, P.; Hunter, D. J.; Paschal, J.; Manolio, T. A.; Tucker, M.; Hoover, R. N.; et al. 2009 · 2009
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Genomic privacy and limits of individual detection in a pool
Sankararaman, S.; Obozinski, G.; Jordan, M. I.; and Halperin, E. 2009 · 2009
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On the difficulties of disclosure prevention in statistical databases or the case for differential privacy
Dwork, C.; and Naor, M. 2010 · 2010
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Differentially private empirical risk minimization
Chaudhuri, K.; Monteleoni, C.; and Sarwate, A. D. 2011 · 2011
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A firm foundation for private data analysis
Dwork, C. 2011 · 2011
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Reading Digits in Natural Images with Unsupervised Feature Learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Houben, S.; Stallkamp, J.; Salmen, J.; Schlipsing, M.; and Igel, C. 2013 · 2013
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The algorithmic foundations of differential privacy
Dwork, C.; Roth, A.; et al. 2014 · 2014
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Fredrikson, M.; Lantz, E.; Jha, S.; Lin, S.; Page, D.; and Ristenpart, T. 2014 · 2014
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The cifar-10 dataset
Krizhevsky, A.; Nair, V.; and Hinton, G. 2014 · 2014
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Efficient estimation of mutual information for strongly dependent variables
Gao, S.; Ver Steeg, G.; and Galstyan, A. 2015 · 2015
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Privacy-preserving deep learning
Shokri, R.; and Shmatikov, V. 2015 · 2015
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Deep learning with differential privacy
Abadi, M.; Chu, A.; Goodfellow, I.; McMahan, H. B.; Mironov, I.; Talwar, K.; and Zhang, L. 2016 · 2016
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Differential privacy as a mutual information constraint
Cuff, P.; and Yu, L. 2016 · 2016
Cited alongside, same era.
On the relation between identifiability, differential privacy, and mutual-information privacy
Wang, W.; Ying, L.; and Zhang, J. 2016 · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Zhang, C.; Bengio, S.; Hardt, M.; Recht, B.; and Vinyals, O. 2016 · 2016
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On the differential privacy of Bayesian inference
Zhang, Z.; Rubinstein, B. I.; and Dimitrakakis, C. 2016 · 2016
Cited alongside, same era.
Dwork, C.; and Feldman, V. 2018 · 2018
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Efficient deep learning on multi-source private data
Hynes, N.; Cheng, R.; and Song, D. 2018 · 2018
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Distributed learning without distress: Privacy-preserving empirical risk minimization
Jayaraman, B.; Wang, L.; Evans, D.; and Gu, Q. 2018 · 2018
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Understanding membership inferences on well-generalized learning models
Long, Y.; Bindschaedler, V.; Wang, L.; Bu, D.; Wang, X.; Tang, H.; Gunter, C. A.; and Chen, K. 2018 · 2018
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Scalable private learning with pate
Papernot, N.; Song, S.; Mironov, I.; Raghunathan, A.; Talwar, K.; and Erlingsson, Ú. 2018 · 2018
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Long, Y.; Bindschaedler, V.; and Gunter, C. A. 2017 · 2017
Cited alongside, same era.
Exploring generalization in deep learning
Neyshabur, B.; Bhojanapalli, S.; McAllester, D.; and Srebro, N. 2017 · 2017
Cited alongside, same era.
Knock knock, who’s there? membership inference on aggregate location data. arXiv
Pyrgelis, A.; Troncoso, C.; and Cristofaro, E. D. 2017 · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Shokri, R.; Stronati, M.; Song, C.; and Shmatikov, V. 2017 · 2017
Cited alongside, same era.
Opening the black box of deep neural networks via information
Shwartz-Ziv, R.; and Tishby, N. 2017 · 2017
Cited alongside, same era.
Machine learning models that remember too much
Song, C.; Ristenpart, T.; and Shmatikov, V. 2017 · 2017
Cited alongside, same era.
Information-theoretic analysis of generalization capability of learning algorithms
Xu, A.; and Raginsky, M. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Membership Inference Attack against Differentially Private Deep Learning Model
Rahman, M. A.; Rahman, T.; Laganière, R.; Mohammed, N.; and Wang, Y. 2018 · 2018
Later among the works it cites.
Towards demystifying membership inference attacks
Truex, S.; Liu, L.; Gursoy, M. E.; Yu, L.; and Wei, W. 2018 · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S.; Giacomelli, I.; Fredrikson, M.; and Jha, S. 2018 · 2018
Later among the works it cites.
Privacy-preserving machine learning through data obfuscation
Zhang, T.; He, Z.; and Lee, R. B. 2018 · 2018
Later among the works it cites.
Socinf: Membership inference attacks on social media health data with machine learning
Liu, G.; Wang, C.; Peng, K.; Huang, H.; Li, Y.; and Cheng, W. 2019 · 2019
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Exploiting unintended feature leakage in collaborative learning
Melis, L.; Song, C.; De Cristofaro, E.; and Shmatikov, V. 2019 · 2019
Later among the works it cites.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M.; Shokri, R.; and Houmansadr, A. 2019 · 2019
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Privacy risks of securing machine learning models against adversarial examples
Song, L.; Shokri, R.; and Mittal, P. 2019 · 2019
Later among the works it cites.