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We design a new algorithm for the Euclidean $k$-means problem that operates in the local model of differential privacy.
Clustering algorithms
John A Hartigan · 1975
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Approximate nearest neighbors: Towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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Practical privacy: The SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Distributed private data analysis: Simultaneously solving how and what
Amos Beimel, Kobbi Nissim, and Eran Omri · 2008
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Private coresets
Dan Feldman, Amos Fiat, Haim Kaplan, and Kobbi Nissim · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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Differentially private combinatorial optimization
Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, and Kunal Talwar · 2010
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Frank McSherry · 2010
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Distributed private heavy hitters
Justin Hsu, Sanjeev Khanna, and Aaron Roth · 2012
Earlier work this paper cites.
Gupt: Privacy preserving data analysis made easy
Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, Dawn Song, and David Culler · 2012
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Local, private, efficient protocols for succinct histograms
Raef Bassily and Adam D. Smith · 2015
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Differentially private subspace clustering
Yining Wang, Yu-Xiang Wang, and Aarti Singh · 2015
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Locating a small cluster privately
Kobbi Nissim, Uri Stemmer, and Salil P. Vadhan · 2016
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k-variates++: more pluses in the k-means++
Richard Nock, Raphaël Canyasse, Roksana Boreli, and Frank Nielsen · 2016
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Differentially private k-means clustering
Dong Su, Jianneng Cao, Ninghui Li, Elisa Bertino, and Hongxia Jin · 2016
Coresets for differentially private k-means clustering and applications to privacy in mobile sensor networks
Dan Feldman, Chongyuan Xiang, Ruihao Zhu, and Daniela Rus · 2017
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Heavy hitters and the structure of local privacy
Mark Bun, Jelani Nelson, and Uri Stemmer · 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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Clustering algorithms for the centralized and local models
Kobbi Nissim and Uri Stemmer · 2018
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The role of interactivity in local differential privacy
Matthew Joseph, Jieming Mao, Seth Neel, and Aaron Roth · 2019
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Cited alongside, same era.
The Complexity of Differential Privacy
Salil Vadhan · 2016
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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 · 2017
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Differentially private clustering in high-dimensional Euclidean spaces
Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, and Hongyang Zhang · 2017
Cited alongside, same era.
Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Thakurta · 2017
Cited alongside, same era.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor
Cited in the paper.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith
Cited in the paper.
Closest in time.
Differentially private clustering: Tight approximation ratios
Badih Ghazi, Ravi Kumar, and Pasin Manurangsi · 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 in one round
Alisa Chang, Badih Ghazi, Ravi Kumar, and Pasin Manurangsi · 2021
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Differentially-private clustering of easy instances
Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, and Eliad Tsfadia · 2021
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