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Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or to the large number of data points that is required for accurate results.
“Privately Learning High-Dimensional Distributions”
Gautam Kamath, Jerry Li, Vikrant Singhal and Jonathan Ullman · 1902
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“On measures of entropy and information”
Alfr“’ed R“’enyi · 1961
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“On Spectral Learning of Mixtures of Distributions”
Dimitris Achlioptas and Frank McSherry · 2005
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“Our Data, Ourselves: Privacy Via Distributed Noise Generation”
Cynthia Dwork et al · 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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“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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“Hypergeometric tail inequalities: ending the insanity”
M Scala · 2009
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“Boosting and Differential Privacy”
Cynthia Dwork, Guy. Rothblum and Salil. Vadhan · 2010
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“The effectiveness of lloyd-type methods for the k-means problem”
Rafail Ostrovsky, Yuval Rabani, Leonard. Schulman and Chaitanya Swamy · 2012
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“Analyzing Graphs with Node Differential Privacy”
Shiva Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova and Adam. Smith · 2013
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“Reservoir Computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring”
Jordi Fonollosa and Ramon Huerta · 2015
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“Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds”
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
“Locating a Small Cluster Privately”
Kobbi Nissim, Uri Stemmer and Salil. Vadhan · 2016
Cited alongside, same era.
“Finite Sample Differentially Private Confidence Intervals”
Vishesh Karwa and Salil Vadhan · 2018
Cited alongside, same era.
“Differentially Private Algorithms for Learning Mixtures of Separated Gaussians”
Gautam Kamath, Or Sheffet, Vikrant Singhal and Jonathan Ullman · 2019
“Private and polynomial time algorithms for learning Gaussians and beyond”
Hassan Ashtiani and Christopher Liaw · 2021
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“Practical Differentially Private Clustering”
Alisa Chang and Pritish Kamath · 2021
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“Instance-optimal Mean Estimation Under Differential Privacy”
Ziyue Huang, Yuting Liang and Ke Yi · 2021
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“A private and computationally-efficient estimator for unbounded gaussians”
Gautam Kamath et al · 2021
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“Private Robust Estimation by Stabilizing Convex Relaxations”
Pravesh Kothari, Pasin Manurangsi and Ameya Velingker · 2021
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“CoinPress: Practical Private Mean and Covariance Estimation”
Sourav Biswas, Yihe Dong, Gautam Kamath and Jonathan. Ullman · 2020
Cited alongside, same era.
“Private mean estimation of heavy-tailed distributions”
Gautam Kamath, Vikrant Singhal and Jonathan Ullman · 2020
Cited alongside, same era.
“Private k-Means Clustering with Stability Assumptions”
Moshe Shechner, Or Sheffet and Uri Stemmer · 2020
Cited alongside, same era.
Input: A database
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Operation:
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Input: A database
Cited in the paper.
“Learning with User-Level Privacy”
Daniel Levy et al · 2021
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“Differentially private algorithms for clustering with stability assumptions”, 2021
Moshe Shechner · 2021
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“Privately learning subspaces”
Vikrant Singhal and Thomas Steinke · 2021
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“Differentially-Private Clustering of Easy Instances”
Edith Cohen et al · 2059
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