Fetching the paper…
Reading the bibliography…
We consider the privacy-preserving machine learning (ML) setting where the trained model must satisfy differential privacy (DP) with respect to the labels of the training examples.
“Randomized response: A survey technique for eliminating evasive answer bias”
Stanley Warner · 1965
Earlier work this paper cites.
“Privacy and democracy in cyberspace”
Paul Schwartz · 1999
Earlier work this paper cites.
“Our data, ourselves: Privacy via distributed noise generation”
Cynthia Dwork et al · 2006
Earlier work this paper cites.
“Calibrating noise to sensitivity in private data analysis”
Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith · 2006
Earlier work this paper cites.
“Efficient Projections onto the ℓ 1 \ell_{1} -ball for learning in high dimensions”
John Duchi, Shai Shalev-Shwartz, Yoram Singer and Tushar Chandra · 2008
Earlier work this paper cites.
“Learning multiple layers of features from tiny images”, 2009
Alex Krizhevsky · 2009
Earlier work this paper cites.
“Sample Complexity Bounds for Differentially Private Learning”
Kamalika Chaudhuri and Daniel Hsu · 2011
Earlier work this paper cites.
“Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks”
Dong-Hyun Lee · 2013
Earlier work this paper cites.
“The geometry of differential privacy: The sparse and approximate cases”
Aleksandar Nikolov, Kunal Talwar and Li Zhang · 2013
Earlier work this paper cites.
Weiran Wang and MiguelÁ. Carreira-Perpiñán · 2013
Earlier work this paper cites.
“Learning with pseudo-ensembles”
Philip Bachman, Ouais Alsharif and Doina Precup · 2014
Earlier work this paper cites.
“The algorithmic foundations of differential privacy.”
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
“RAPPOR: Randomized aggregatable privacy-preserving ordinal response”
Úlfar Erlingsson, Vasyl Pihur and Aleksandra Korolova · 2014
Cited alongside, same era.
“Preserving statistical validity in adaptive data analysis”
Cynthia Dwork et al · 2015
Cited alongside, same era.
“Private learning and sanitization: pure vs. approximate differential privacy”
Amos Beimel, Kobbi Nissim and Uri Stemmer · 2016
Cited alongside, same era.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Cited alongside, same era.
“Wide residual networks”
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
“A closer look at memorization in deep networks”
Devansh Arpit et al · 2017
Cited alongside, same era.
“MixMatch: A holistic approach to semi-supervised learning”
David Berthelot et al · 2019
Later among the works it cites.
“The Secret Sharer: Evaluating and testing unintended memorization in neural networks”
Nicholas Carlini et al · 2019
Later among the works it cites.
Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan and Shuang Song · 2019
Later among the works it cites.
“Private Selection from Private Candidates”
Jingcheng Liu and Kunal Talwar · 2019
Later among the works it cites.
“On sparse linear regression in the local differential privacy model”
Di Wang and Jinhui Xu · 2019
Later among the works it cites.
“RandAugment: Practical automated data augmentation with a reduced search space”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Semi-supervised knowledge transfer for deep learning from private training data”
Nicolas Papernot et al · 2017
Cited alongside, same era.
“Membership inference attacks against machine learning models”
Reza Shokri, Marco Stronati, Congzheng Song and Vitaly Shmatikov · 2017
Cited alongside, same era.
“The complexity of differential privacy”
Salil Vadhan · 2017
Cited alongside, same era.
“Understanding deep learning requires rethinking generalization”
Chiyuan Zhang et al · 2017
Cited alongside, same era.
“Scalable private learning with PATE” Code available from https://github.com/tensorflow/privacy/tree/master/research/pate_2018 under Apache 2.0 license
Nicolas Papernot et al · 2018
Cited alongside, same era.
“Privacy risk in machine learning: Analyzing the connection to overfitting”
Samuel Yeom, Irene Giacomelli, Matt Fredrikson and Somesh Jha · 2018
Cited alongside, same era.
Ekin Cubuk, Barret Zoph, Jonathon Shlens and Quoc Le · 2020
Later among the works it cites.
“Does learning require memorization? A short tale about a long tail”
Vitaly Feldman · 2020
Later among the works it cites.
“Auditing differentially private machine learning: How private is private SGD?”
Matthew Jagielski, Jonathan Ullman and Alina Oprea · 2020
Later among the works it cites.
“FixMatch: Simplifying semi-supervised learning with consistency and confidence”
Kihyuk Sohn et al · 2020
Later among the works it cites.
“Extracting training data from large language models”
Nicholas Carlini et al · 2021
Closest in time.
“On deep learning with label differential privacy”, 2021
Badih Ghazi et al · 2021
Closest in time.
“Adversary instantiation: Lower bounds for differentially private machine learning”
Milad Nasr et al · 2021
Closest in time.