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Auditing algorithms' privacy typically involves simulating a game-based protocol that guesses which of two adjacent datasets was the original input.
Ix. on the problem of the most efficient tests of statistical hypotheses
Jerzy Neyman and Egon Sharpe Pearson · 1933
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The use of confidence or fiducial limits illustrated in the case of the binomial
Charles J Clopper and Egon S Pearson · 1934
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Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
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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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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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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
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On significance of the least significant bits for differential privacy
Ilya Mironov · 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 Smith, and Abhradeep Thakurta · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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On the privacy properties of variants on the sparse vector technique
Yan Chen and Ashwin Machanavajjhala · 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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Privtree: A differentially private algorithm for hierarchical decompositions
Jun Zhang, Xiaokui Xiao, and Xing Xie · 2016
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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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Lightdp: Towards automating differential privacy proofs
Danfeng Zhang and Daniel Kifer · 2017
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Dp-finder: Finding differential privacy violations by sampling and optimization
Benjamin Bichsel, Timon Gehr, Dana Drachsler-Cohen, Petar Tsankov, and Martin Vechev · 2018
Cited alongside, same era.
Detecting violations of differential privacy
Zeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, and Daniel Kifer · 2018
Cited alongside, same era.
Private selection from private candidates
Jingcheng Liu and Kunal Talwar · 2019
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Deciding differential privacy for programs with finite inputs and outputs
Gilles Barthe, Rohit Chadha, Vishal Jagannath, A Prasad Sistla, and Mahesh Viswanathan · 2020
Composition of differential privacy & privacy amplification by subsampling
Thomas Steinke · 2022
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Backpropagation clipping for deep learning with differential privacy
Timothy Stevens, Ivoline C Ngong, David Darais, Calvin Hirsch, David Slater, and Joseph P Near · 2022
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Optimal accounting of differential privacy via characteristic function
Yuqing Zhu, Jinshuo Dong, and Yu-Xiang Wang · 2022
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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 · 2023
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Privacy-preserving sparse generalized eigenvalue problem
Lijie Hu, Zihang Xiang, Jiabin Liu, and Di Wang · 2023
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Cited alongside, same era.
Coupled relational symbolic execution
Gian Pietro Farina · 2020
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Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
Cited alongside, same era.
Sok: Differential privacy as a causal property
Michael Carl Tschantz, Shayak Sen, and Anupam Datta · 2020
Cited alongside, same era.
Checkdp: An automated and integrated approach for proving differential privacy or finding precise counterexamples
Yuxin Wang, Zeyu Ding, Daniel Kifer, and Danfeng Zhang · 2020
Cited alongside, same era.
Dp-sniper: Black-box discovery of differential privacy violations using classifiers
Benjamin Bichsel, Samuel Steffen, Ilija Bogunovic, and Martin Vechev · 2021
Cited alongside, same era.
Gaussian differential privacy
Jinshuo Dong, Aaron Roth, and Weijie Su · 2021
Cited alongside, same era.
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Group and attack: Auditing differential privacy
Johan Lokna, Anouk Paradis, Dimitar I Dimitrov, and Martin Vechev · 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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Sok: Let the privacy games begin! a unified treatment of data inference privacy in machine learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, and Santiago Zanella-Béguelin · 2023
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Privacy auditing with one (1) training run
Thomas Steinke, Milad Nasr, and Matthew Jagielski · 2023
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Practical differentially private and byzantine-resilient federated learning
Zihang Xiang, Tianhao Wang, Wanyu Lin, and Di Wang · 2023
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Bayesian estimation of differential privacy
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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Differentially private natural language models: Recent advances and future directions
Lijie Hu, Ivan Habernal, Lei Shen, and Di Wang · 2024
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Auditing f f -differential privacy in one run
Saeed Mahloujifar, Luca Melis, and Kamalika Chaudhuri · 2024
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Hoeffding’s inequality — Wikipedia, the free encyclopedia
Wikipedia · 2024
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Preserving node-level privacy in graph neural networks
Zihang Xiang, Tianhao Wang, and Di Wang · 2024
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Ppml-omics: a privacy-preserving federated machine learning method protects patients’ privacy in omic data
Juexiao Zhou, Siyuan Chen, Yulian Wu, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, et al · 2024
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