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Reseach Article

Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining

by Sumana M, Dr Hareesh K S
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 10
Year of Publication: 2010
Authors: Sumana M, Dr Hareesh K S
10.5120/1113-1457

Sumana M, Dr Hareesh K S . Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining. International Journal of Computer Applications. 6, 10 ( September 2010), 1-5. DOI=10.5120/1113-1457

@article{ 10.5120/1113-1457,
author = { Sumana M, Dr Hareesh K S },
title = { Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { September 2010 },
volume = { 6 },
number = { 10 },
month = { September },
year = { 2010 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume6/number10/1113-1457/ },
doi = { 10.5120/1113-1457 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:55:01.291887+05:30
%A Sumana M
%A Dr Hareesh K S
%T Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 6
%N 10
%P 1-5
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Privacy Preserving Data mining techniques depends on privacy, which captures what information is sensitive in the original data and should therefore be protected from either direct or indirect disclosure. Secrecy and anonymity are useful ways of thinking about privacy. This privacy should be measureable and entity to be considered private should be valuable. In this paper, we discuss the various anonymization techniques that can be used for privatizing data. The goal of anonymization is to secure access to confidential information while at the same time releasing aggregate information to the public. The challenge in each of the techniques is to protect data so that they can be published without revealing confidential information that can be linked to specific individuals. Also protection is to be achieved with minimum loss of the accuracy sought by database users. Different approaches of anonymization have been discussed and a comparison of the same has been provided.

References
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  2. Latanya Sweeney “Achieving k-anonymity Privacy Protection Using Generalization and Suppression”,May 2002, International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 571-588.
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  6. Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian, t-Closeness: Privacy Beyond k-Anonymity and ℓ-Diversity, Citiseer 2007
Index Terms

Computer Science
Information Sciences

Keywords

Data preprocessing K-anonymity Quasi-identifier