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Reseach Article

Privacy Preserved Data Publishing Techniques for Tabular Data

by Keerthy C., Sabitha S.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 9
Year of Publication: 2016
Authors: Keerthy C., Sabitha S.
10.5120/ijca2016911874

Keerthy C., Sabitha S. . Privacy Preserved Data Publishing Techniques for Tabular Data. International Journal of Computer Applications. 151, 9 ( Oct 2016), 1-6. DOI=10.5120/ijca2016911874

@article{ 10.5120/ijca2016911874,
author = { Keerthy C., Sabitha S. },
title = { Privacy Preserved Data Publishing Techniques for Tabular Data },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 9 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume151/number9/26258-2016911874/ },
doi = { 10.5120/ijca2016911874 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:56:36.962831+05:30
%A Keerthy C.
%A Sabitha S.
%T Privacy Preserved Data Publishing Techniques for Tabular Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 9
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Almost all countries have imposed strict laws on the disclosure of Personally Identifiable Information(PII). However PII need to be published for many purposes like research. In such cases, we apply different types of methods like anonymization, encryption etc. This paper discuss about the different methods of anonymization of tabular microdata. The most popular method of data anonymization of tabular data is k-anonymity. However, it suffers from many attacks and hence l-diversity was proposed. The l-diversity anonymization also possessed various limitations and hence t-closeness was proposed. This paper summarize these anonymization techniques and their limitations.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Data anonymization k-anonymity l-diversity t-closeness