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Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
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Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature

by Fuad Ali Mohammed Al-yarimi, Sonajharia Minz
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 3
Year of Publication: 2012
Authors: Fuad Ali Mohammed Al-yarimi, Sonajharia Minz
10.5120/9676-4103

Fuad Ali Mohammed Al-yarimi, Sonajharia Minz . Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature. International Journal of Computer Applications. 60, 3 ( December 2012), 40-47. DOI=10.5120/9676-4103

@article{ 10.5120/9676-4103,
author = { Fuad Ali Mohammed Al-yarimi, Sonajharia Minz },
title = { Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 3 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 40-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number3/9676-4103/ },
doi = { 10.5120/9676-4103 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:41.772372+05:30
%A Fuad Ali Mohammed Al-yarimi
%A Sonajharia Minz
%T Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 3
%P 40-47
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Privacy preserving for data engineering methods like mining and publishing etc. , with the advancement of the rapid development of technologies like Internet and distributed computing has turned out to be one of the most important research areas of interest and has also triggered a serious issue of concern in accordance with the personal data usage in the recent times. Effective analysis result and gathering accurate data is desired by data users in specific, in contrast to the data owners who are concerned as their data contains personal information like the ones in government departments, Health insurance organizations and hospitals and data mining and warehouse utilities, where privacy is an issue to be taken rather seriously. Hence various proposals have been designated in data engineering methods publishing and mining for the purpose of preserving privacy. This paper briefs about the classification of the various privacy preserving approaches in data engineering, scans the current state of the art in lieu of preserving privacy of data, as also reviewing of the pros and cons of these specified approaches.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Data publishing privacy preserving anonymity data engineering k-anonymity t-closeness l-diversity

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