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Reseach Article

Determining t in t-closeness using Multiple Sensitive Attributes

by Debaditya Roy, Sanjay Kumar Jena
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 19
Year of Publication: 2013
Authors: Debaditya Roy, Sanjay Kumar Jena

Debaditya Roy, Sanjay Kumar Jena . Determining t in t-closeness using Multiple Sensitive Attributes. International Journal of Computer Applications. 70, 19 ( May 2013), 47-51. DOI=10.5120/12179-8291

@article{ 10.5120/12179-8291,
author = { Debaditya Roy, Sanjay Kumar Jena },
title = { Determining t in t-closeness using Multiple Sensitive Attributes },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 19 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { },
doi = { 10.5120/12179-8291 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T21:33:19.785532+05:30
%A Debaditya Roy
%A Sanjay Kumar Jena
%T Determining t in t-closeness using Multiple Sensitive Attributes
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 19
%P 47-51
%D 2013
%I Foundation of Computer Science (FCS), NY, USA

Over the years, t-closeness has been dealt with in great detail in Privacy Preserving Data Publishing and Mining. Other methods like k-anonymity fail in terms of attribute disclosure and background knowledge attack as demonstrated by many papers in this field. l-diversity also fails in case of skewness attack. t-closenesstakes care of all these shortcomings and is the most robust privacy model known till date. However, till now t-closeness was only applied upon a single sensitive attribute. Here, a novel way in determining t and applying t-closeness for multiple sensitive attributes is presented. The only information required beforehand is the partitioning classes of Sensitive Attribute(s). Since, t-closeness is generally applied on anonymized datasets, it is imperative to know the t values beforehand so as to unnecessarily anonymize data beyond requirement. The rationale of using the measure of determining t is discussed with conclusive proof and speedup achieved is also shown.

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Index Terms

Computer Science
Information Sciences


Privacy Preserving Data Mining Privacy Preserving Data Publishing t-closeness Multiple Sensitive Attributes