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Reseach Article

Survey on Recent Developments in Privacy Preserving Models

by Sowmyarani C N, Dr. G N Srinivasan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 9
Year of Publication: 2012
Authors: Sowmyarani C N, Dr. G N Srinivasan
10.5120/4716-6884

Sowmyarani C N, Dr. G N Srinivasan . Survey on Recent Developments in Privacy Preserving Models. International Journal of Computer Applications. 38, 9 ( January 2012), 18-22. DOI=10.5120/4716-6884

@article{ 10.5120/4716-6884,
author = { Sowmyarani C N, Dr. G N Srinivasan },
title = { Survey on Recent Developments in Privacy Preserving Models },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 9 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number9/4716-6884/ },
doi = { 10.5120/4716-6884 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:58.188213+05:30
%A Sowmyarani C N
%A Dr. G N Srinivasan
%T Survey on Recent Developments in Privacy Preserving Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 9
%P 18-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Privacy preserving in data mining [1] is one of the major and increasingly interested area of research under data security. Privacy will be provided for data at different levels such as, while publishing the data, at the time of retrieving result by preserving sensitive data without disclosing it. It is not just sufficient to preserve sensitive data without disclosing it, but also need to manipulate and present data so that, certain inference channels are blocked. Numbers of techniques are proposed to achieve privacy protection for sensitive data. But, most of these methods are facing side effects such as reduced utility, less accuracy, data mining efficiency down-graded, disclosure risk, etc. In this paper we analyze all these different techniques how they handle data in turn to provide privacy and points out their merits and demerits.

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Index Terms

Computer Science
Information Sciences

Keywords

k-anonymity l-diversity p-sensitive privacy