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We prove new lower bounds for statistical estimation tasks under the constraint of $(\varepsilon, \delta)$-differential privacy.
Über die abgrenzung der Eigenwerte einer matrix
S. Gershgorin · 1931
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Assouad, Fano, and Le Cam
Bin Yu · 1997
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Collusion-secure fingerprinting for digital data
Dan Boneh and James Shaw · 1998
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Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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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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Optimal probabilistic fingerprint codes
Gabor Tardos · 2008
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Lecture notes for Bayesian modeling and inference, 2010
Michael Jordan · 2010
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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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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 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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Analyze Gauss: Optimal bounds for privacy-preserving principal component analysis
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
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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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Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 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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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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Differentially private testing of identity and closeness of discrete distributions
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2018
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The total variation distance between high-dimensional Gaussians
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
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Vishesh Karwa and Salil Vadhan · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
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Gautam Kamath and Jonathan Ullman · 2020
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Learning discrete distributions: User vs item-level privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, and Michael Riley · 2020
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Optimal private median estimation under minimal distributional assumptions
Christos Tzamos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, and Ilias Zadik · 2020
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On differentially private stochastic convex optimization with heavy-tailed data
Di Wang, Hanshen Xiao, Srinivas Devadas, and Jinhui Xu · 2020
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Privately learning Markov random fields
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, and Zhiwei Steven Wu · 2020
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Roman Vershynin · 2018
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The power of the hybrid model for mean estimation
Brendan Avent, Yatharth Dubey, and Aleksandra Korolova · 2019
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Differentially private sub-Gaussian location estimators
Marco Avella-Medina and Victor-Emmanuel Brunel · 2019
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Private hypothesis selection
Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu · 2019
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Average-case averages: Private algorithms for smooth sensitivity and mean estimation
Mark Bun and Thomas Steinke · 2019
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The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T. Tony Cai, Yichen Wang, and Linjun Zhang · 2019
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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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On the sample complexity of privately learning unbounded high-dimensional gaussians
Ishaq Aden-Ali, Hassan Ashtiani, and Gautam Kamath · 2021
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Privately learning mixtures of axis-aligned gaussians
Ishaq Aden-Ali, Hassan Ashtiani, and Christopher Liaw · 2021
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Private and polynomial time algorithms for learning Gaussians and beyond
Hassan Ashtiani and Christopher Liaw · 2021
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Differentially private Assouad, Fano, and Le Cam
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2021
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Covariance-aware private mean estimation without private covariance estimation
Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthinou · 2021
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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 · 2021
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Instance-optimal mean estimation under differential privacy
Ziyue Huang, Yuting Liang, and Ke Yi · 2021
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Improved rates for differentially private stochastic convex optimization with heavy-tailed data
Gautam Kamath, Xingtu Liu, and Huanyu Zhang · 2021
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A private and computationally-efficient estimator for unbounded gaussians
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman · 2021
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Differential privacy and robust statistics in high dimensions
Xiyang Liu, Weihao Kong, and Sewoong Oh · 2021
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Learning with user-level privacy
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh · 2021
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On the absolute constant in hanson-wright inequality
Kamyar Moshksar · 2021
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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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