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In today's data-driven world, the sensitivity of information has been a significant concern.
Practical privacy: the sulq framework,
A. Blum, C. Dwork, F. McSherry, K. Nissim, · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis,
C. Dwork, F. McSherry, K. Nissim, A. Smith, · 2006
Earlier work this paper cites.
Mechanism design via differential privacy,
F. McSherry, K. Talwar, · 2007
Earlier work this paper cites.
Smooth sensitivity and sampling in private data analysis,
K. Nissim, S. Raskhodnikova, A. Smith, · 2007
Earlier work this paper cites.
Uci machine learning repository, university of california, irvine, school of information and computer sciences,
A. Asuncion, · 2007
Earlier work this paper cites.
Differential privacy: A survey of results,
C. Dwork, · 2008
Cited alongside, same era.
A. Narayanan, Data privacy: The non-interactive setting, The University of Texas at Austin, 2009
2009
Cited alongside, same era.
Differentially private m-estimators,
J. Lei, · 2011
Cited alongside, same era.
A firm foundation for private data analysis,
C. Dwork, · 2011
Cited alongside, same era.
Gupt: privacy preserving data analysis made easy,
P. Mohan, A. Thakurta, E. Shi, D. Song, D. Culler, · 2012
Cited alongside, same era.
Privgene: differentially private model fitting using genetic algorithms,
J. Zhang, X. Xiao, Y. Yang, Z. Zhang, M. Winslett, · 2013
Later among the works it cites.
Differentially private k-means clustering,
D. Su, J. Cao, N. Li, E. Bertino, H. Jin, · 2016
Later among the works it cites.
Differentially private k-means clustering and a hybrid approach to private optimization,
D. Su, J. Cao, N. Li, E. Bertino, M. Lyu, H. Jin, · 2017
Later among the works it cites.
Differentially private k-means clustering with convergence guarantee,
Z. Lu, H. Shen, · 2020
Later among the works it cites.
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