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Clustering is a fundamental problem in data analysis.
“Privately Learning High-Dimensional Distributions”
Gautam Kamath, Jerry Li, Vikrant Singhal and Jonathan Ullman · 1902
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“A Bound on Tail Probabilities for Quadratic Forms in Independent Random Variables”
David Hanson and Farroll Wright · 1971
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“Extensions of Lipschitz maps into a Hilbert space”
William Johnson and Joram Lindenstrauss · 1984
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“A Two-Round Variant of EM for Gaussian Mixtures”
Sanjoy Dasgupta and Leonard. Schulman · 2000
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“Learning mixtures of arbitrary gaussians”
Arora Sanjeev and Ravi Kannan · 2001
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“A spectral algorithm for learning mixture models”
Santosh Vempala and Grant Wang · 2004
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“On Spectral Learning of Mixtures of Distributions”
Dimitris Achlioptas and Frank McSherry · 2005
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“Practical Privacy: The SuLQ Framework”
Avrim Blum, Cynthia Dwork, Frank McSherry and Kobbi Nissim · 2005
Earlier work this paper cites.
“Practical privacy: the SuLQ framework”
Avrim Blum, Cynthia Dwork, Frank McSherry and Kobbi Nissim · 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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“k-means++ the advantages of careful seeding”
David Arthur and Sergei Vassilvitskii · 2007
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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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“Approximate clustering without the approximation”
Maria-Florina Balcan, Avrim Blum and Anupam Gupta · 2009
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“Private coresets”
Dan Feldman, Amos Fiat, Haim Kaplan and Kobbi Nissim · 2009
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“Privacy integrated queries: an extensible platform for privacy-preserving data analysis”
Frank McSherry · 2009
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“Stability yields a PTAS for k-median and k-means clustering”
Pranjal Awasthi, Avrim Blum and Or Sheffet · 2010
Earlier work this paper cites.
“Bounds on the Sample Complexity for Private Learning and Private Data Release”
Amos Beimel, Shiva Kasiviswanathan and Kobbi Nissim · 2010
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“Boosting and Differential Privacy”
Cynthia Dwork, Guy. Rothblum and Salil. Vadhan · 2010
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“Differentially Private Combinatorial Optimization”
Anupam Gupta et al · 2010
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“Clustering with spectral norm and the k-means algorithm”
Amit Kumar and Ravindran Kannan · 2010
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“What Can We Learn Privately?”
Shiva Kasiviswanathan et al · 2011
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“Center-based clustering under perturbation stability”
Pranjal Awasthi, Avrim Blum and Or Sheffet · 2012
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“Are stable instances easy?”
Yonatan Bilu and Nathan Linial · 2012
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“GUPT: Privacy Preserving Data Analysis Made Easy”
Prashanth Mohan et al · 2012
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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
Cited alongside, same era.
“On learning mixtures of well-separated gaussians”
Oded Regev and Aravindan Vijayaraghavan · 2017
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“List-decodable robust mean estimation and learning mixtures of spherical gaussians”
Ilias Diakonikolas, Daniel Kane and Alistair Stewart · 2018
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“Mixture models, robustness, and sum of squares proofs”
Samuel Hopkins and Jerry Li · 2018
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“Optimal differentially private algorithms for k-means clustering”
Zhiyi Huang and Jinyan Liu · 2018
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“Differentially Private k-Means with Constant Multiplicative Error”
Haim Kaplan and Uri Stemmer · 2018
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“Robust moment estimation and improved clustering via sum of squares”
Pravesh Kothari, Jacob Steinhardt and David Steurer · 2018
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Amin Coja-Oghlan · 2013
Cited alongside, same era.
“A Near-Optimal Algorithm for Differentially-Private Principal Components.”
Kamalika Chaudhuri, Anand Sarwate and Kaushik Sinha · 2013
Cited alongside, same era.
“On differentially private low rank approximation”
Michael Kapralov and Kunal Talwar · 2013
Cited alongside, same era.
“Analyze Gauss: Optimal Bounds for Privacy-preserving Principal Component Analysis”
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta and Li Zhang · 2014
Cited alongside, same era.
“Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures”
Ananda Suresh, Alon Orlitsky, Jayadev Acharya and Ashkan Jafarpour · 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.
Later among the works it cites.
“Finite Sample Differentially Private Confidence Intervals”
Vishesh Karwa and Salil Vadhan · 2018
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“Clustering Algorithms for the Centralized and Local Models”
Kobbi Nissim and Uri Stemmer · 2018
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“Graph-based Clustering under Differential Privacy”
Rafael Pinot et al · 2018
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“Better guarantees for k-means and euclidean k-median by primal-dual algorithms”
Sara Ahmadian, Ashkan Norouzi-Fard, Ola Svensson and Justin Ward · 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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T Cai, Yichen Wang and Linjun Zhang · 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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“CoinPress: Practical Private Mean and Covariance Estimation”
Sourav Biswas, Yihe Dong, Gautam Kamath and Jonathan. Ullman · 2020
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“Differentially Private Clustering: Tight Approximation Ratios”
Badih Ghazi, Ravi Kumar and Pasin Manurangsi · 2020
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“Private mean estimation of heavy-tailed distributions”
Gautam Kamath, Vikrant Singhal and Jonathan Ullman · 2020
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“A note on differentially private clustering with large additive error”
Huy. Nguyen · 2020
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“Private k-Means Clustering with Stability Assumptions”
Moshe Shechner, Or Sheffet and Uri Stemmer · 2020
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“Locally Private k-Means Clustering”
Uri Stemmer · 2020
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“Private hypothesis selection”
Mark Bun, Gautam Kamath, Thomas Steinke and Zhiwei Wu · 2021
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