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We provide an approximation algorithm for k-means clustering in the one-round (aka non-interactive) local model of differential privacy (DP).
A short note on concentration inequalities for random vectors with subGaussian norm
C. Jin, P. Netrapalli, R. Ge, S. M. Kakade, and M. I. Jordan · 1902
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Lattice coverings of space
C. A. Rogers · 1959
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Similarity estimation techniques from rounding algorithms
M. Charikar · 2002
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Numerical resolution of an “unbalanced” mass transport problem
J.-D. Benamou · 2003
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On coresets for k k -means and k k -median clustering
S. Har-Peled and S. Mazumdar · 2004
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Almost perfect lattices, the covering radius problem, and applications to Ajtai’s connection factor
D. Micciancio · 2004
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LSH forest: self-tuning indexes for similarity search
M. Bawa, T. Condie, and P. Ganesan · 2005
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Practical privacy: the sulq framework
A. Blum, C. Dwork, F. McSherry, and K. Nissim · 2005
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k-means++: The advantages of careful seeding
D. Arthur and S. Vassilvitskii · 2006
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Fast construction of nets in low-dimensional metrics and their applications
S. Har-Peled and M. Mendel · 2006
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Cryptography from anonymity
Y. Ishai, E. Kushilevitz, R. Ostrovsky, and A. Sahai · 2006
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Smooth sensitivity and sampling in private data analysis
K. Nissim, S. Raskhodnikova, and A. Smith · 2007
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Adaptive sampling for k k -means clustering
A. Aggarwal, A. Deshpande, and R. Kannan · 2009
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NP-hardness of Euclidean sum-of-squares clustering
D. Aloise, A. Deshpande, P. Hansen, and P. Popat · 2009
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Private coresets
D. Feldman, A. Fiat, H. Kaplan, and K. Nissim · 2009
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Differentially private combinatorial optimization
A. Gupta, K. Ligett, F. McSherry, A. Roth, and K. Talwar · 2010
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GUPT: privacy preserving data analysis made easy
P. Mohan, A. Thakurta, E. Shi, D. Song, and D. Culler · 2012
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Local privacy and statistical minimax rates
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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A deterministic single exponential time algorithm for most lattice problems based on Voronoi cell computations
D. Micciancio and P. Voulgaris · 2013
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Ú. Erlingsson, V. Pihur, and A. Korolova · 2014
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Generalized Wasserstein distance and its application to transport equations with source
B. Piccoli and F. Rossi · 2014
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How Google tricks itself to protect Chrome user privacy
S. Shankland · 2014
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Differentially private subspace clustering
Y. Wang, Y.-X. Wang, and A. Singh · 2015
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Apple’s “differential privacy” is about collecting your data – but not your data
A. Greenberg · 2016
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Locating a small cluster privately
K. Nissim, U. Stemmer, and S. P. Vadhan · 2016
Cited alongside, same era.
The privacy blanket of the shuffle model
B. Balle, J. Bell, A. Gascón, and K. Nissim · 2019
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Distributed differential privacy via shuffling
A. Cheu, A. D. Smith, J. Ullman, D. Zeber, and M. Zhilyaev · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta · 2019
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Performance of Johnson–Lindenstrauss transform for k k -means and k k -medians clustering
K. Makarychev, Y. Makarychev, and I. P. Razenshteyn · 2019
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Introducing TensorFlow Privacy: Learning with Differential Privacy for Training Data, March 2019
C. Radebaugh and U. Erlingsson · 2019
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Better guarantees for k k -means and Euclidean k k -median by primal-dual algorithms
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k k -variates++: more pluses in the k k -means++
R. Nock, R. Canyasse, R. Boreli, and F. Nielsen · 2016
Cited alongside, same era.
Differentially private k k -means clustering
D. Su, J. Cao, N. Li, E. Bertino, and H. Jin · 2016
Cited alongside, same era.
LSH forest: Practical algorithms made theoretical
A. Andoni, I. P. Razenshteyn, and N. S. Nosatzki · 2017
Cited alongside, same era.
Learning with privacy at scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
Differentially private clustering in high-dimensional Euclidean spaces
M. Balcan, T. Dick, Y. Liang, W. Mou, and H. Zhang · 2017
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
A. Bittau, Ú. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnés, and B. Seefeld · 2017
Cited alongside, same era.
S. Ahmadian, A. Norouzi-Fard, O. Svensson, and J. Ward · 2020
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Separating local & shuffled differential privacy via histograms
V. Balcer and A. Cheu · 2020
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Private summation in the multi-message shuffle model
B. Balle, J. Bell, A. Gascón, and K. Nissim · 2020
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Practical locally private heavy hitters
R. Bassily, K. Nissim, U. Stemmer, and A. Thakurta · 2020
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The discrete Gaussian for differential privacy
C. Canonne, G. Kamath, and T. Steinke · 2020
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Locally private k -means clustering
U. Stemmer · 2020
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PyTorch Differential Privacy Series Part 1: DP-SGD Algorithm Explained, August 2020
D. Testuggine and I. Mironov · 2020
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Distributed k k -means clustering guaranteeing local differential privacy
C. Xia, J. Hua, W. Tong, and S. Zhong · 2020
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Connecting robust shuffle privacy and pan-privacy
V. Balcer, A. Cheu, M. Joseph, and J. Mao · 2021
Closest in time.
Differentially private k k -means clustering via exponential mechanism and max cover
A. Chaturvedi, H. Nguyen, and E. Xu · 2021
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On distributed differential privacy and counting distinct elements
L. Chen, B. Ghazi, R. Kumar, and P. Manurangsi · 2021
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Differentially private clustering via maximum coverage
M. Jones, H. L. Nguyen, and T. Nguyen · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation
P. Kairouz, Z. Liu, and T. Steinke · 2021
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