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Machine learning models can be trained with formal privacy guarantees via differentially private optimizers such as DP-SGD.
Ix. on the problem of the most efficient tests of statistical hypotheses
Jerzy Neyman, Egon Sharpe Pearson, and Karl Pearson · 1933
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The use of Confidence or Fiducial Limits Illustrated in the Case of the Binomial
C. J. Clopper and E. S. Pearson · 1934
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Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
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Sparse spatial autoregressions
R Kelley Pace and Ronald Barry · 1997
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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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 D. 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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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 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
Cited alongside, same era.
Privacy amplification by mixing and diffusion mechanisms
Borja Balle, Gilles Barthe, Marco Gaboardi, and Joseph Geumlek · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Y. Hannun, and Laurens van der Maaten · 2020
Cited alongside, same era.
Gaussian differential privacy
Jinshuo Dong, Aaron Roth, and Weijie J. Su · 2022
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Connect the dots: Tighter discrete approximations of privacy loss distributions
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi · 2022
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Reconstructing training data from trained neural networks
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, and Michal Irani · 2022
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A general framework for auditing differentially private machine learning
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, and Brian Testa · 2022
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Differentially private learning needs hidden state (or much faster convergence)
Jiayuan Ye and Reza Shokri · 2022
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Privacy loss of noisy stochastic gradient descent might converge even for non-convex losses
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Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
Cited alongside, same era.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Cited alongside, same era.
Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
Cited alongside, same era.
Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
Cited alongside, same era.
Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Songi, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlin · 2021
Cited alongside, same era.
Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
Cited alongside, same era.
Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2021
Cited alongside, same era.
Shahab Asoodeh and Mario Diaz · 2023
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CANIFE: Crafting canaries for empirical privacy measurement in federated learning
Samuel Maddock, Alexandre Sablayrolles, and Pierre Stock · 2023
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Tight auditing of differentially private machine learning
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, and Andreas Terzis · 2023
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Privacy auditing with one (1) training run
Thomas Steinke, Milad Nasr, and Matthew Jagielski · 2023
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Bayesian estimation of differential privacy, 2023
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Rühle, Andrew Paverd, Mohammad Naseri, Boris Köpf, and Daniel Jones · 2023
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Low-cost high-power membership inference by boosting relativity, 2023
Sajjad Zarifzadeh, Philippe Liu, and Reza Shokri · 2023
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One-shot empirical privacy estimation for federated learning
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, and Vinith Suriyakumar · 2024
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It’s our loss: No privacy amplification for hidden state dp-sgd with non-convex loss, 2024
Meenatchi Sundaram Muthu Selva Annamalai · 2024
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Nearly tight black-box auditing of differentially private machine learning
Meenatchi Sundaram Muthu Selva Annamalai and Emiliano De Cristofaro · 2024
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Shifted interpolation for differential privacy
Jinho Bok, Weijie J Su, and Jason Altschuler · 2024
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Stochastic gradient langevin unlearning
Eli Chien, Haoyu Wang, Ziang Chen, and Pan Li · 2024
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Privacy backdoors: Stealing data with corrupted pretrained models
Shanglun Feng and Florian Tramèr · 2024
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Unified enhancement of privacy bounds for mixture mechanisms via f f -differential privacy
Chendi Wang, Buxin Su, Jiayuan Ye, Reza Shokri, and Weijie Su · 2024
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