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In this work, we focus on improving the accuracy-variance trade-off for state-of-the-art differentially private machine learning (DP ML) methods.
The need for biases in learning generalizations
Tom M Mitchell · 1980
Earlier work this paper cites.
Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, and Abhradeep Thakurta · 2001
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 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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Faster differentially private samplers via rényi divergence analysis of discretized langevin MCMC
Arun Ganesh and Kunal Talwar · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Giuseppe Ateniese, Giovanni Felici, Luigi V Mancini, Angelo Spognardi, Antonio Villani, and Domenico Vitali · 2013
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 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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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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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
Earlier work this paper cites.
Privacy preserving rbf kernel support vector machine
Haoran Li, Li Xiong, Lucila Ohno-Machado, and Xiaoqian Jiang · 2014
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Rényi divergence and kullback-leibler divergence
Tim van Erven and Peter Harremos · 2014
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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
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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On the differential privacy of bayesian inference
Zuhe Zhang, Benjamin IP Rubinstein, and Christos Dimitrakakis · 2016
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Checkpoint ensembles: Ensemble methods from a single training process
Hugh Chen, Scott Lundberg, and Su-In Lee · 2017
Earlier work this paper cites.
Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2017
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Rényi differential privacy
Ilya Mironov · 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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Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang · 2017
Cited alongside, same era.
A hitting time analysis of stochastic gradient langevin dynamics
Yuchen Zhang, Percy Liang, and Moses Charikar · 2017
Cited alongside, same era.
Differentially private significance tests for regression coefficients
Andrés F. Barrientos, Jerome P. Reiter, Ashwin Machanavajjhala, and Yan Chen · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne · 2018
Cited alongside, same era.
Bootstrap inference and differential privacy: Standard errors for free
Thomas Brawner and James Honaker · 2018
Cited alongside, same era.
Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Úlfar Erlingsson · 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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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Differentially private learning needs better features (or much more data)
Florian Tramer and Dan Boneh · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz · 2018
Cited alongside, same era.
Machine learning with membership privacy using adversarial regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
Inherent brain segmentation quality control from fully convnet monte carlo sampling
Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, and Christian Wachinger · 2018
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The need for uncertainty quantification in machine-assisted medical decision making
Edmon Begoli, Tanmoy Bhattacharya, and Dimitri Kusnezov · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Cited alongside, same era.
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, et al · 2021
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Differentially private learning with adaptive clipping
Galen Andrew, Om Thakkar, Brendan McMahan, and Swaroop Ramaswamy · 2021
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High-performance large-scale image recognition without normalization
Andy Brock, Soham De, Samuel L. Smith, and Karen Simonyan · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Differential privacy dynamics of langevin diffusion and noisy gradient descent
Rishav Chourasia, Jiayuan Ye, and Reza Shokri · 2021
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Making the last iterate of sgd information theoretically optimal
Prateek Jain, Dheeraj M. Nagaraj, and Praneeth Netrapalli · 2021
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Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
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Membership privacy for machine learning models through knowledge transfer
Virat Shejwalkar and Amir Houmansadr · 2021
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Membership inference attacks against nlp classification models
Virat Shejwalkar, Huseyin A Inan, Amir Houmansadr, and Robert Sim · 2021
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Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
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Differentially private bayesian neural networks on accuracy, privacy and reliability, 2021
Qiyiwen Zhang, Zhiqi Bu, Kan Chen, and Qi Long · 2021
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Diurnal or nocturnal? federated learning of multi-branch networks from periodically shifting distributions
Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečnỳ, Andrew Hard, and Tom Goldstein · 2021
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Public data-assisted mirror descent for private model training
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, and Abhradeep Thakurta · 2022
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
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Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
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Private ad modeling with dp-sgd
Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V Varadarajan, and Chiyuan Zhang · 2022
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Improved differential privacy for sgd via optimal private linear operators on adaptive streams
Sergey Denisov, Brendan McMahan, Keith Rush, Adam Smith, and Abhradeep Guha Thakurta · 2022
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2022
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Parametric bootstrap for differentially private confidence intervals
Cecilia Ferrando, Shufan Wang, and Daniel Sheldon · 2022
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The StackOverflow data
Kaggle · 2022
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Property inference from poisoning
Saeed Mahloujifar, Esha Ghosh, and Melissa Chase · 2022
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Federated learning with formal differential privacy guarantees
Brendan McMahan, Abhradeep Thakurta, Galen Andrew, Borja Balle, Peter Kairouz, Daniel Ramage, Shuang Song, Thomas Steinke, Andreas Terzis, Om Thakkar, and Zheng Xu · 2022
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Hyperparameter tuning with renyi differential privacy
Nicolas Papernot and Thomas Steinke · 2022
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Differential privacy guarantees for stochastic gradient langevin dynamics
Théo Ryffel, Francis Bach, and David Pointcheval · 2022
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Mitigating membership inference attacks by { \{ Self-Distillation } \} through a novel ensemble architecture
Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, and Prateek Mittal · 2022
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Training differentially private ad prediction models with semi-sensitive features
Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, et al · 2024
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