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Reseach Article

Statistical Disclosure Control for Data Privacy Preservation

by Sarat Kumar Chettri, Bonani Paul, Ajoy Krishna Dutta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 10
Year of Publication: 2013
Authors: Sarat Kumar Chettri, Bonani Paul, Ajoy Krishna Dutta
10.5120/13899-1880

Sarat Kumar Chettri, Bonani Paul, Ajoy Krishna Dutta . Statistical Disclosure Control for Data Privacy Preservation. International Journal of Computer Applications. 80, 10 ( October 2013), 38-43. DOI=10.5120/13899-1880

@article{ 10.5120/13899-1880,
author = { Sarat Kumar Chettri, Bonani Paul, Ajoy Krishna Dutta },
title = { Statistical Disclosure Control for Data Privacy Preservation },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 10 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number10/13899-1880/ },
doi = { 10.5120/13899-1880 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:12.991544+05:30
%A Sarat Kumar Chettri
%A Bonani Paul
%A Ajoy Krishna Dutta
%T Statistical Disclosure Control for Data Privacy Preservation
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 10
%P 38-43
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the phenomenal change in a way data are collected, stored and disseminated among various data analyst there is an urgent need of protecting the privacy of data. As when individual data get disseminated among various users, there is a high risk of revelation of sensitive data related to any individual, which may violate various legal and ethical issues. Statistical Disclosure Control (SDC) is often applied to statistical databases for preserving the privacy of individual data. Microaggregation is an efficient Statistical Disclosure Control perturbative technique for microdata protection i. e. protection of individual data. Unlike k-Anonymity, microaggregation method modifies data without suppressing or generalizing it. But to prevent the disclosure of sensitive data it should not be modified to an extent that the data utility is affected. So, the major challenge is how to perturb the data in such a way that a balance is maintained between data utility and risk of data disclosure. Here in this paper, we have proposed a new SDC method based on multivariate data-oriented microaggregation technique for individual data protection with minimal information loss and low data disclosure risk. Experimental results show that our proposed method proves our claim as when compared with other state-of art existing methods of data protection.

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Index Terms

Computer Science
Information Sciences

Keywords

SDC Microaggregation information loss data disclosure risk microdata perturbative k-Anonymity. .