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Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed.
“Membership inference attacks from first principles”
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis and Florian Tramer · 1914
Earlier work this paper cites.
“On measures of entropy and information”
Alfr“’ed R“’enyi · 1961
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“Rademacher and Gaussian complexities: Risk bounds and structural results”
Peter Bartlett and Shahar Mendelson · 2002
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“Simplified PAC-Bayesian Margin Bounds”
David. McAllester · 2003
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“Our data, ourselves: Privacy via distributed noise generation”
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov and Moni Naor · 2006
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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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“Privacy via pseudorandom sketches”
Nina Mishra and Mark Sandler · 2006
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“Mechanism Design via Differential Privacy”
Frank McSherry and Kunal Talwar · 2007
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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 and robust statistics”
Cynthia Dwork and Jing Lei · 2009
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“Accurate estimation of the degree distribution of private networks”
Michael Hay, Chao Li, Gerome Miklau and David Jensen · 2009
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“Releasing search queries and clicks privately”
Aleksandra Korolova, Krishnaram Kenthapadi, Nina Mishra and Alexandros Ntoulas · 2009
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“Computational differential privacy”
Ilya Mironov, Omkant Pandey, Omer Reingold and Salil Vadhan · 2009
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“Pan-Private Streaming Algorithms.”
Cynthia Dwork, Moni Naor, Toniann Pitassi, Guy Rothblum and Sergey Yekhanin · 2010
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“A multiplicative weights mechanism for privacy-preserving data analysis”
Moritz Hardt and Guy Rothblum · 2010
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“On the geometry of differential privacy”
Moritz Hardt and Kunal Talwar · 2010
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“Privacy integrated queries: an extensible platform for privacy-preserving data analysis”
Frank McSherry · 2010
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“Noiseless database privacy”
Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman and Abhradeep Thakurta · 2011
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“Sample complexity bounds for differentially private learning”
Kamalika Chaudhuri and Daniel Hsu · 2011
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Rob Hall, Alessandro Rinaldo and Larry Wasserman · 2011
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“What Can We Learn Privately?”
Shiva Kasiviswanathan, Homin. Lee, Kobbi Nissim, Sofya Raskhodnikova and Adam. Smith · 2011
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“No free lunch in data privacy”
Daniel Kifer and Ashwin Machanavajjhala · 2011
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“PCPs and the hardness of generating synthetic data”
Jonathan Ullman and Salil Vadhan · 2011
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“Distributed Private Heavy Hitters”
Justin Hsu, Sanjeev Khanna and Aaron Roth · 2012
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“A simple and practical algorithm for differentially private data release”
Moritz Hardt, Katrina Ligett and Frank McSherry · 2012
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“Geo-indistinguishability: Differential privacy for location-based systems”
Miguel Andr“’es, Nicol“’as Bordenabe, Konstantinos Chatzikokolakis and Catuscia Palamidessi · 2013
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“Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy”
Raef Bassily, Adam Groce, Jonathan Katz and Adam Smith · 2013
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“Broadening the scope of differential privacy using metrics”
Konstantinos Chatzikokolakis, Miguel Andr“’es, Nicol“’as Bordenabe and Catuscia Palamidessi · 2013
Earlier work this paper cites.
“Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation”
John. Duchi, Martin. Wainwright and Michael. Jordan · 2013
Earlier work this paper cites.
“Privacy via the Johnson–Lindenstrauss transform”
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov and Nina Mishra · 2013
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“The geometry of differential privacy: the approximate and sparse cases”
Aleksandar Nikolov, Kunal Talwar and Li Zhang · 2013
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“Bounds on the sample complexity for private learning and private data release”
Amos Beimel, Hai Brenner, Shiva Kasiviswanathan and Kobbi Nissim · 2014
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“Fingerprinting codes and the price of approximate differential privacy”
Mark Bun, Jonathan Ullman and Salil Vadhan · 2014
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“Using convex relaxations for efficiently and privately releasing marginals”
Cynthia Dwork, Aleksandar Nikolov and Kunal Talwar · 2014
Earlier work this paper cites.
“RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response”
“’Ulfar Erlingsson, Vasyl Pihur and Aleksandra Korolova · 2014
Cited alongside, same era.
“Blowfish privacy: Tuning privacy-utility trade-offs using policies”
Xi He, Ashwin Machanavajjhala and Bolin Ding · 2014
Cited alongside, same era.
“Pufferfish: A framework for mathematical privacy definitions”
Daniel Kifer and Ashwin Machanavajjhala · 2014
Cited alongside, same era.
“On the’semantics’ of differential privacy: A Bayesian formulation”
Shiva Kasiviswanathan and Adam Smith · 2014
Cited alongside, same era.
“Differentially Private Release and Learning of Threshold Functions”
Mark Bun, Kobbi Nissim, Uri Stemmer and Salil. Vadhan · 2015
Cited alongside, same era.
“Local, Private, Efficient Protocols for Succinct Histograms”
Raef Bassily and Adam. Smith · 2015
“Simultaneous Private Learning of Multiple Concepts”
Mark Bun, Kobbi Nissim and Uri Stemmer · 2019
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“Capacity bounded differential privacy”
Kamalika Chaudhuri, Jacob Imola and Ashwin Machanavajjhala · 2019
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“The secret sharer: Evaluating and testing unintended memorization in neural networks”
Nicholas Carlini, Chang Liu, “’Ulfar Erlingsson, Jernej Kos and Dawn Song · 2019
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“Distributed differential privacy via shuffling”
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber and Maxim Zhilyaev · 2019
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Damien Desfontaines and Bal“’azs Pej“’o · 2019
Later among the works it cites.
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Cited alongside, same era.
“Efficient algorithms for privately releasing marginals via convex relaxations”
Cynthia Dwork, Aleksandar Nikolov and Kunal Talwar · 2015
Cited alongside, same era.
“Robust traceability from trace amounts”
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman and Salil Vadhan · 2015
Cited alongside, same era.
“Sample Complexity Bounds on Differentially Private Learning via Communication Complexity”
Vitaly Feldman and David Xiao · 2015
Cited alongside, same era.
“Bounds on the expectation of the maximum of samples from a gaussian”
Gautam Kamath · 2015
Cited alongside, same era.
“Optimality of the Laplace mechanism in differential privacy”
Fragkiskos Koufogiannis, Shuo Han and George Pappas · 2015
Cited alongside, same era.
“Social graph publishing with privacy guarantees”
Faraz Ahmed, Alex Liu and Rong Jin · 2016
Cited alongside, same era.
Jinshuo Dong, Aaron Roth and Weijie Su · 2019
Later among the works it cites.
“Amplification by shuffling: From local to central differential privacy via anonymity”
“’Ulfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar and Abhradeep Thakurta · 2019
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“Per-instance differential privacy”
Yu-Xiang Wang · 2019
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“Context Aware Local Differential Privacy”
Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage and Ziteng Sun · 2020
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“Separating Local & Shuffled Differential Privacy via Histograms”
Victor Balcer and Albert Cheu · 2020
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“The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise”
Ilias Diakonikolas, Daniel. Kane and Pasin Manurangsi · 2020
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“Auditing differentially private machine learning: How private is private SGD?”
Matthew Jagielski, Jonathan Ullman and Alina Oprea · 2020
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“Privately Learning Thresholds: Closing the Exponential Gap”
Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor and Uri Stemmer · 2020
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“Learning discrete distributions: user vs item-level privacy”
Yuhan Liu, Ananda Suresh, Felix Yu, Sanjiv Kumar and Michael Riley · 2020
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“Facebook Privacy-Protected Full URLs Data Set”
Solomon Messing, Christina DeGregorio, Bennett Hillenbrand, Gary King, Saurav Mahanti, Zagreb Mukerjee, Chaya Nayak, Nate Persily, Bogdan State and Arjun Wilkins · 2020
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“Multi-Central Differential Privacy”
Thomas Steinke · 2020
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“The Pitfalls of Average-Case Differential Privacy” https://differentialprivacy.org/average-case-dp/ , DifferentialPrivacy.org, 2020
Thomas Steinke and Jonathan Ullman · 2020
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“Why Privacy Needs Composition” https://differentialprivacy.org/privacy-composition/ , DifferentialPrivacy.org, 2020
Thomas Steinke and Jonathan Ullman · 2020
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“Bayesian differential privacy for machine learning”
Aleksei Triastcyn and Boi Faltings · 2020
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“Connecting Robust Shuffle Privacy and Pan-Privacy”
Victor Balcer, Albert Cheu, Matthew Joseph and Jieming Mao · 2021
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“Statistical Inference is Not a Privacy Violation” https://differentialprivacy.org/inference-is-not-a-privacy-violation/ , DifferentialPrivacy.org, 2021
Mark Bun, Damien Desfontaines, Cynthia Dwork, Moni Naor, Kobbi Nissim, Aaron Roth, Adam Smith, Thomas Steinke, Jonathan Ullman and Salil Vadhan · 2021
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“Census Bureau Sets Key Parameters to Protect Privacy in 2020 Census Results” https://www.census.gov/newsroom/press-releases/2021/2020-census-key-parameters.html , 2021
US Bureau · 2021
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“Privacy-loss Budget Allocation 2021-06-08” https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/01-Redistricting_File--PL_94-171/2021-06-08_ppmf_Production_Settings/2021-06-08-privacy-loss_budgetallocation.pdf , 2021
US Bureau · 2021
Later among the works it cites.
“Mean Estimation with User-level Privacy under Data Heterogeneity”
Rachel Cummings, Vitaly Feldman, Audra McMillan and Kunal Talwar · 2021
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“A list of real-world uses of differential privacy” https://desfontain.es/privacy/real-world-differential-privacy.html , 2021
Damien Desfontaines · 2021
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“Demystifying the US Census Bureau’s reconstruction attack” Ted is writing things (personal blog), https://desfontain.es/privacy/us-census-reconstruction-attack.html , 2021
Damien Desfontaines · 2021
Later among the works it cites.
“User-Level Differentially Private Learning via Correlated Sampling”
Badih Ghazi, Ravi Kumar and Pasin Manurangsi · 2021
Later among the works it cites.
“Robust and Private Learning of Halfspaces”
Badih Ghazi, Ravi Kumar, Pasin Manurangsi and Thao Nguyen · 2021
Later among the works it cites.
“Learning with User-Level Privacy”
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri and Ananda Suresh · 2021
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“A Better Privacy Analysis of the Exponential Mechanism” https://differentialprivacy.org/exponential-mechanism-bounded-range/ , DifferentialPrivacy.org, 2021
Ryan Rogers and Thomas Steinke · 2021
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“The 2020 Census Disclosure Avoidance System TopDown Algorithm”
John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Daniel Kifer, Philip Leclerc and Ashwin Machanavajjhala · 2022
Closest in time.
“On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data”
Florimond Houssiau, Luc Rocher and Yves-Alexandre de Montjoye · 2022
Closest in time.
“Debugging Differential Privacy: A Case Study for Privacy Auditing”
Florian Tramer, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski and Nicholas Carlini · 2022
Closest in time.