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We study person-level differentially private (DP) mean estimation in the case where each person holds multiple samples.
Nonuniform Central Limit Bounds with Applications to Probabilities of Deviations
R. Michel · 1976
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Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2005
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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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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V. Pearson, Dietrich A. Stephan, Stanley F. Nelson, and David W. Craig · 2008
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Differential privacy under continual observation
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N Rothblum · 2010
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Private and continual release of statistics
TH Hubert Chan, Elaine Shi, and Dawn Song · 2010
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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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Private graphon estimation for sparse graphs
Christian Borgs, Jennifer Chayes, and Adam Smith · 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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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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Lecture notes in information-theoretic methods in high-dimensional statistics, lecture 14: Packing, covering, and consequences on minimax risk, Spring 2016
Yihong Wu · 2016
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Sub-sampled cubic regularization for non-convex optimization
Jonas Moritz Kohler and Aurelien Lucchi · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Revealing network structure, confidentially: Improved rates for node-private graphon estimation
Christian Borgs, Jennifer Chayes, Adam Smith, and Ilias Zadik · 2018
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2018
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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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Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman · 2019
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Differentially private algorithms for learning mixtures of separated Gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman · 2019
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Efficiently estimating Erdos-Renyi graphs with node differential privacy
Adam Sealfon and Jonathan Ullman · 2019
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Coinpress: Practical private mean and covariance estimation
Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman · 2020
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Private identity testing for high-dimensional distributions
Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, and Lydia Zakynthinou · 2020
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Differentially private confidence intervals
Wenxin Du, Canyon Foot, Monica Moniot, Andrew Bray, and Adam Groce · 2020
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Private mean estimation of heavy-tailed distributions
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman · 2020
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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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Tight and robust private mean estimation with few users
Shyam Narayanan, Vahab Mirrokni, and Hossein Esfandiari · 2022
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Generalized resilience and robust statistics
Banghua Zhu, Jiantao Jiao, and Jacob Steinhardt · 2022
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Polynomial time and private learning of unbounded gaussian mixture models
Jamil Arbas, Hassan Ashtiani, and Christopher Liaw · 2023
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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
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Gautam Kamath and Jonathan Ullman · 2020
Cited alongside, same era.
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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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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Plan: variance-aware private mean estimation
Martin Aumüller, Christian Janos Lebeda, Boel Nelson, and Rasmus Pagh · 2023
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Discrete distribution estimation under user-level local differential privacy
Jayadev Acharya, Yuhan Liu, and Ziteng Sun · 2023
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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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Stability is stable: Connections between replicability, privacy, and adaptive generalization
Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, and Jessica Sorrell · 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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User-level private stochastic convex optimization with optimal rates
Raef Bassily and Ziteng Sun · 2023
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Robust estimation of discrete distributions under local differential privacy
Julien Chhor and Flore Sentenac · 2023
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On user-level private convex optimization
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, and Chiyuan Zhang · 2023
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User-level differential privacy with few examples per user
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, and Chiyuan Zhang · 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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A bias-variance-privacy trilemma for statistical estimation
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman · 2023
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Better and simpler lower bounds for differentially private statistical estimation
Shyam Narayanan · 2023
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Mixtures of gaussians are privately learnable with a polynomial number of samples
Mohammad Afzali, Hassan Ashtiani, and Christopher Liaw · 2024
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Private graphon estimation via sum-of-squares
Hongjie Chen, Jingqiu Ding, Tommaso d’Orsi, Yiding Hua, Chih-Hung Liu, and David Steurer · 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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User-level differentially private stochastic convex optimization: Efficient algorithms with optimal rates
Daogao Liu and Hilal Asi · 2024
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Victor S Portella and Nick Harvey · 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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