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We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 1912
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The influence curve and its role in robust estimation
Frank R Hampel · 1974
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Obtaining confidence intervals for the risk ratio in cohort studies
DJSM Katz, J Baptista, SP Azen, and MC Pike · 1978
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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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Testing statistical hypotheses
Erich L Lehmann and Joseph P Romano · 2006
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
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Consumer credit-risk models via machine-learning algorithms
Amir E Khandani, Adlar J Kim, and Andrew W Lo · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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On significance of the least significant bits for differential privacy
Ilya Mironov · 2012
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Differentially private naive bayes classification
Jaideep Vaidya, Basit Shafiq, Anirban Basu, and Yuan Hong · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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O privacy, where art thou?: Genomics and privacy
Aleksandra Slavkovic and Fei Yu · 2015
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Recommended confidence intervals for two independent binomial proportions
Morten W Fagerland, Stian Lydersen, and Petter Laake · 2015
Cited alongside, same era.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 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.
Differentially private random decision forests using smooth sensitivity
Sam Fletcher and Md Zahidul Islam · 2017
Cited alongside, same era.
Detecting violations of differential privacy
Zeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, and Daniel Kifer · 2018
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Optimal subsampling with influence functions
Daniel Ting and Eric Brochu · 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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Data poisoning against differentially-private learners: Attacks and defenses
Yuzhe Ma, Xiaojin Zhu Zhu, and Justin Hsu · 2019
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Diffprivlib: the ibm differential privacy library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
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Auditing differentially private machine learning: How private is private sgd?
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Machine learning and genomics: precision medicine versus patient privacy
C-A Azencott · 2018
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The role of differential privacy in gdpr compliance
Rachel Cummings and Deven Desai · 2018
Cited alongside, same era.
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
Cited in the paper.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song
Cited in the paper.
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
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Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini · 2021
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Dp-sniper: Black-box discovery of differential privacy violations using classifiers
Benjamin Bichsel, Samuel Steffen, Ilija Bogunovic, and Martin Vechev · 2021
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Opacus: User-friendly differential privacy library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov · 2021
Later among the works it cites.