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Privacy aware K-means clustering with high utility

Nguyen, Thanh Dai, Gupta, Sunil, Rana, Santu and Venkatesh, Svetha 2016, Privacy aware K-means clustering with high utility. In Bailey, James, Khan, Latifur, Washio, Takashi, Dobbie, Gillian, Huang, Joshua Zhexue and Wang, Ruili (ed), Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Springer, Berlin, Germany, pp.388-400, doi: 10.1007/978-3-319-31750-2_31.

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Title Privacy aware K-means clustering with high utility
Author(s) Nguyen, Thanh Dai
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Title of book Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I
Editor(s) Bailey, James
Khan, Latifur
Washio, Takashi
Dobbie, Gillian
Huang, Joshua Zhexue
Wang, Ruili
Publication date 2016
Series Lecture notes in artificial intelligence; v.9652
Chapter number 31
Total chapters 44
Start page 388
End page 400
Total pages 13
Publisher Springer
Place of Publication Berlin, Germany
Summary Privacy-preserving data mining aims to keep data safe, yet useful. But algorithms providing strong guarantees often end up with low utility. We propose a novel privacy preserving framework that thwarts an adversary from inferring an unknown data point by ensuring that the estimation error is almost invariant to the inclusion/exclusion of the data point. By focusing directly on the estimation error of the data point, our framework is able to significantly lower the perturbation required. We use this framework to propose a new privacy aware K-means clustering algorithm. Using both synthetic and real datasets, we demonstrate that the utility of this algorithm is almost equal to that of the unperturbed K-means, and at strict privacy levels, almost twice as good as compared to the differential privacy counterpart.
ISBN 9783319317533
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-31750-2_31
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083253

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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