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Differential privacy (DP) is a rigorous notion of data privacy, used for private statistics.
Some Problems in Minimax Point Estimation
J. L. Hodges and E. L. Lehmann · 1950
Earlier work this paper cites.
Complex analysis: an introduction to the theory of analytic functions of one complex variable
Lars V Ahlfors · 1953
Earlier work this paper cites.
Information and the accuracy attainable in the estimation of statistical parameters
Calyampudi Radhakrishna Rao · 1992
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Collusion-secure fingerprinting for digital data
Dan Boneh and James Shaw · 1998
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Mathematical Methods of Statistics
Harald Cramér · 1999
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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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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Socr data - 25,000 records of human heights (in) and weights (lbs)
Ivo Dinov · 2008
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Optimal probabilistic fingerprint codes
Gabor Tardos · 2008
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Universally utility-maximizing privacy mechanisms
Arpita Ghosh, Tim Roughgarden, and Mukund Sundararajan · 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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Completeness, similar regions, and unbiased estimation-part i
Erich Leo Lehmann and Henry Scheffé · 2011
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University of california report on 2011 employee pay
University of California · 2011
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Local privacy and statistical minimax rates
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
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Privacy and statistical risk: Formalisms and minimax bounds
Rina Foygel Barber and John C Duchi · 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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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Preventing false discovery in interactive data analysis is hard
Moritz Hardt and Jonathan Ullman · 2014
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On the’semantics’ of differential privacy: A bayesian formulation
Shiva P Kasiviswanathan and Adam Smith · 2014
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The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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Differentially private learning of structured discrete distributions
Ilias Diakonikolas, Moritz Hardt, and Ludwig Schmidt · 2015
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Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
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Interactive fingerprinting codes and the hardness of preventing false discovery
Thomas Steinke and Jonathan Ullman · 2015
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
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Locating a small cluster privately
Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2016
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Max-information, differential privacy, and post-selection hypothesis testing
Ryan Rogers, Aaron Roth, Adam Smith, and Om Thakkar · 2016
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Make up your mind: The price of online queries in differential privacy
Mark Bun, Thomas Steinke, and Jonathan Ullman · 2017
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Minimax optimal procedures for locally private estimation
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2017
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Generalization for adaptively-chosen estimators via stable median
Vitaly Feldman and Thomas Steinke · 2017
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Between pure and approximate differential privacy
Thomas Steinke and Jonathan Ullman · 2017
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Tight lower bounds for differentially private selection
Thomas Steinke and Jonathan Ullman · 2017
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The complexity of differential privacy
Salil Vadhan · 2017
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2018
Cited alongside, same era.
The power of the hybrid model for mean estimation
Brendan Avent, Yatharth Dubey, and Aleksandra Korolova · 2019
Cited alongside, same era.
Bounding user contributions: A bias-variance trade-off in differential privacy
Kareem Amin, Alex Kulesza, Andres Munoz, and Sergei Vassilvitskii · 2019
Cited alongside, same era.
Differentially private sub-Gaussian location estimators
Marco Avella-Medina and Victor-Emmanuel Brunel · 2019
Cited alongside, same era.
The privacy blanket of the shuffle model
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019
Cited alongside, same era.
Private hypothesis selection
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 2019
Bias and variance of post-processing in differential privacy
Keyu Zhu, Pascal Van Hentenryck, and Ferdinando Fioretto · 2021
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Private and polynomial time algorithms for learning Gaussians and beyond
Hassan Ashtiani and Christopher Liaw · 2022
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Archimedes meets privacy: On privately estimating quantiles in high dimensions under minimal assumptions
Omri Ben-Eliezer, Dan Mikulincer, and Ilias Zadik · 2022
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Private estimation with public data
Alex Bie, Gautam Kamath, and Vikrant Singhal · 2022
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Statistically valid inferences from privacy protected data
Georgina Evans, Gary King, Margaret Schwenzfeier, and Abhradeep Thakurta · 2022
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
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Cited alongside, same era.
Average-case averages: Private algorithms for smooth sensitivity and mean estimation
Mark Bun and Thomas Steinke · 2019
Cited alongside, same era.
Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
Cited alongside, same era.
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.
Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
Cited alongside, same era.
Differentially private algorithms for learning mixtures of separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman · 2019
Cited alongside, same era.
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
Hilal Asi and John C Duchi · 2020
Cited alongside, same era.
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2022
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Parametric bootstrap for differentially private confidence intervals
Cecilia Ferrando, Shufan Wang, and Daniel Sheldon · 2022
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Privacy induces robustness: Information-computation gaps and sparse mean estimation
Kristian Georgiev and Samuel B Hopkins · 2022
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Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism
Samuel B Hopkins, Gautam Kamath, and Mahbod Majid · 2022
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Improved rates for differentially private stochastic convex optimization with heavy-tailed data
Gautam Kamath, Xingtu Liu, and Huanyu Zhang · 2022
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New lower bounds for private estimation and a generalized fingerprinting lemma
Gautam Kamath, Argyris Mouzakis, and Vikrant Singhal · 2022
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A private and computationally-efficient estimator for unbounded gaussians
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman · 2022
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Private robust estimation by stabilizing convex relaxations
Pravesh K Kothari, Pasin Manurangsi, and Ameya Velingker · 2022
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Differential privacy and robust statistics in high dimensions
Xiyang Liu, Weihao Kong, and Sewoong Oh · 2022
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Concentration of the exponential mechanism and differentially private multivariate medians
Kelly Ramsay, Aukosh Jagannath, and Shoja’eddin Chenouri · 2022
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Composition of differential privacy & privacy amplification by subsampling, 2022
Thomas Steinke · 2022
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Friendlycore: Practical differentially private aggregation
Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, and Uri Stemmer · 2022
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Post-processing of differentially private data: A fairness perspective
Keyu Zhu, Ferdinando Fioretto, and Pascal Van Hentenryck · 2022
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Privately estimating a Gaussian: Efficient, robust and optimal
Daniel Alabi, Pravesh K Kothari, Pranay Tankala, Prayaag Venkat, and Fred Zhang · 2023
Closest in time.
From robustness to privacy and back
Hilal Asi, Jonathan Ullman, and Lydia Zakynthinou · 2023
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Private distribution learning with public data: The view from sample compression
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, and Vikrant Singhal · 2023
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Fast, sample-efficient, affine-invariant private mean and covariance estimation for subgaussian distributions
Gavin Brown, Samuel B Hopkins, and Adam Smith · 2023
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Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen, Vincent Cohen-Addad, Tommaso d’Orsi, Alessandro Epasto, Jacob Imola, David Steurer, and Stefan Tiegel · 2023
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Score attack: A lower bound technique for optimal differentially private learning
T Tony Cai, Yichen Wang, and Linjun Zhang · 2023
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Stronger privacy amplification by shuffling for Rényi and approximate differential privacy
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2023
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Robustness implies privacy in statistical estimation
Samuel B Hopkins, Gautam Kamath, Mahbod Majid, and Shyam Narayanan · 2023
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A pretty fast algorithm for adaptive private mean estimation
Rohith Kuditipudi, John Duchi, and Saminul Haque · 2023
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Privacy and bias analysis of disclosure avoidance systems
Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck, Saswat Das, and Christine Task · 2023
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Private mean estimation with person-level differential privacy
Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, and Jonathan Ullman · 2024
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A feasibility study of differentially private summary statistics and regression analyses with evaluations on administrative and survey data
Andrés F Barrientos, Aaron R Williams, Joshua Snoke, and Claire McKay Bowen · 2024
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Continual mean estimation under user-level privacy
Anand Jerry George, Lekshmi Ramesh, Aditya Vikram Singh, and Himanshu Tyagi · 2024
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General Gaussian noise mechanisms and their optimality for unbiased mean estimation
Aleksandar Nikolov and Haohua Tang · 2024
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A Huber loss minimization approach to mean estimation under user-level differential privacy
Puning Zhao, Lifeng Lai, Li Shen, Qingming Li, Jiafei Wu, and Zhe Liu · 2024
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