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Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, $\epsilon$, about how much information is leaked by a mechanism.
Privacy-preserving collaborative deep learning with irregular participants
Lingchen Zhao, Yan Zhang, Qian Wang, Yanjiao Chen, Cong Wang, and Qin Zou · 1902
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On the limited memory BFGS method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
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Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
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A modified BFGS method and its global convergence in nonconvex minimization
Dong-Hui Li and Masao Fukushima · 2001
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Differential Privacy: A Survey of Results
Cynthia Dwork · 2008
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Nils Homer et al · 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 · 2009
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Differentially private recommender systems: Building privacy into the Netflix prize contenders
Frank McSherry and Ilya Mironov · 2009
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Discovering frequent patterns in sensitive data
Raghav Bhaskar, Srivatsan Laxman, Adam Smith, and Abhradeep Thakurta · 2010
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Data mining with differential privacy
Arik Friedman and Assaf Schuster · 2010
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Private record matching using differential privacy
Ali Inan, Murat Kantarcioglu, Gabriel Ghinita, and Elisa Bertino · 2010
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Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers
Manas Pathak, Shantanu Rane, and Bhiksha Raj · 2010
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Differentially private Empirical Risk Minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Differentially private online learning
Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta · 2012
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PrivBasis: Frequent itemset mining with differential privacy
Ninghui Li, Wahbeh Qardaji, Dong Su, and Jianneng Cao · 2012
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ADADELTA: An adaptive learning rate method
Matthew D Zeiler · 2012
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Functional mechanism: Regression analysis under differential privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett · 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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Differentially private learning with kernels
Prateek Jain and Abhradeep Thakurta · 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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Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso
Adam Smith and Abhradeep Thakurta · 2013
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Cited alongside, same era.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
(Near) Dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Guha Thakurta · 2014
Cited alongside, same era.
Acquire Valued Shoppers Challenge
Kaggle, Inc · 2014
Cited alongside, same era.
Private Empirical Risk Minimization beyond the worst case: The effect of the constraint set geometry
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
Cited alongside, same era.
Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Munoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C. Lupu, and Fabio Roli · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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DP-EM: Differentially private expectation maximization
Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, and Max Welling · 2017
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Preserving differential privacy in convolutional deep belief networks
NhatHai Phan, Xintao Wu, and Dejing Dou · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Giuseppe Ateniese, Luigi Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici · 2015
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.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
Nearly Optimal Private LASSO
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2015
Cited alongside, same era.
Support vector machines under adversarial label contamination
Huang Xiao, Battista Biggio, Blaine Nelson, Han Xiao, Claudia Eckert, and Fabio Roli · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Di Wang, Minwei Ye, and Jinhui Xu · 2017
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Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey Naughton · 2017
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Generative poisoning attack method against neural networks
Chaofei Yang, Qing Wu, Hai Li, and Yiran Chen · 2017
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Efficient private ERM for smooth objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou, and Liwei Wang · 2017
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2018
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Privacy-preserving distributed deep learning for clinical data
Brett K Beaulieu-Jones, William Yuan, Samuel G Finlayson, and Zhiwei Steven Wu · 2018
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Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
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Detecting violations of differential privacy
Zeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, and Daniel Kifer · 2018
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Property inference attacks on fully connected neural networks using permutation invariant representations
Karan Ganju, Qi Wang, Wei Yang, Carl A Gunter, and Nikita Borisov · 2018
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DP-ADMM: ADMM-based distributed learning with differential privacy
Zonghao Huang, Rui Hu, Yanmin Gong, and Eric Chan-Tin · 2018
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Efficient deep learning on multi-source private data
Nick Hynes, Raymond Cheng, and Dawn Song · 2018
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Distributed learning without distress: Privacy-preserving Empirical Risk Minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2018
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Membership inference attack against differentially private deep learning model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganière, Noman Mohammed, and Yang Wang · 2018
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Stealing hyperparameters in machine learning
Binghui Wang and Neil Zhenqiang Gong · 2018
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Cache telepathy: Leveraging shared resource attacks to learn DNN architectures
Mengjia Yan, Christopher Fletcher, and Josep Torrellas · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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The Secret Sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song · 2019
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Towards practical differentially private convex optimization
Roger Iyengar, Joseph P Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang · 2019
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Investigating statistical privacy frameworks from the perspective of hypothesis testing
Changchang Liu, Xi He, Thee Chanyaswad, Shiqiang Wang, and Prateek Mittal · 2019
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Differentially private model publishing for deep learning
Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, and Stacey Truex · 2019
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