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Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training regimes.
Brown corpus manual
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Calibrating noise to sensitivity in private data analysis
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Mechanism design via differential privacy
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On the complexity of differentially private data release: Efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N. Rothblum, and Salil Vadhan · 2009
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Sample complexity bounds for differentially private learning
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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(Near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Guha Thakurta · 2014
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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The composition theorem for differential privacy
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Nearly-optimal private LASSO
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Efficient private empirical risk minimization for high-dimensional learning
Shiva Prasad Kasiviswanathan and Hongxia Jin · 2016
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Learning with privacy at scale
Differential Privacy Team, Apple · 2017
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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Mitigating data sparsity using similarity reinforcement-enhanced collaborative filtering
Yan Hu, Weisong Shi, Hong Li, and Xiaohui Hu · 2017
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Understanding the sparse vector technique for differential privacy
Min Lyu, Dong Su, and Ninghui Li · 2017
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Tight lower bounds for differentially private selection
Thomas Steinke and Jonathan Ullman · 2017
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T. Tony Cai, Yichen Wang, and Linjun Zhang · 2019
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Practical differentially private top-k selection with pay-what-you-get composition
David Durfee and Ryan M Rogers · 2019
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Practical differentially private top- k k selection with pay-what-you-get composition
David Durfee and Ryan M. Rogers · 2019
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Deep personalized re-targeting
Meisam Hejazinia, Pavlos Mitsoulis-Ntompos, and Serena Zhang · 2019
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Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 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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Differentially private mixture of generative neural networks
Gergely Acs, Luca Melis, Claude Castelluccia, and Emiliano De Cristofaro · 2018
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cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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Privacy preserving synthetic data release using deep learning
Nazmiye Ceren Abay, Yan Zhou, Murat Kantarcioglu, Bhavani Thuraisingham, and Latanya Sweeney · 2018
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Bargav Jayaraman and David Evans · 2019
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A simple deep personalized recommendation system
Pavlos Mitsoulis-Ntompos, Meisam Hejazinia, Serena Zhang, and Travis Brady · 2019
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R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
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Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H Brendan McMahan · 2019
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dpugc: Learn differentially private representation for user generated contents
XS Vu, SN Tran, and L Jiang · 2019
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Subsampled Rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
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Differentially private empirical risk minimization with non-convex loss functions
Di Wang, Changyou Chen, and Jinhui Xu · 2019
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Efficient privacy-preserving nonconvex optimization
Lingxiao Wang, Bargav Jayaraman, David Evans, and Quanquan Gu · 2019
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Extracting training data from large language models, 2020
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel · 2020
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A new analysis of differential privacy’s generalization guarantees
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld · 2020
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Differentially private deep learning with direct feedback alignment
Jaewoo Lee and Daniel Kifer · 2020
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https://github.com/pytorch/opacus , August 2020
Introducing opacus: A high-speed library for training pytorch models with differential privacy · 2020
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Training production language models without memorizing user data
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays · 2020
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Bypassing the ambient dimension: Private SGD with gradient subspace identification
Yingxue Zhou, Zhiwei Steven Wu, and Arindam Banerjee · 2020
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