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Privacy Preserving Techniques in Social Networks Data Publishing - A Review

by Amardeep Singh, Divya Bansal, Sanjeev Sofat
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 15
Year of Publication: 2014
Authors: Amardeep Singh, Divya Bansal, Sanjeev Sofat
10.5120/15282-3880

Amardeep Singh, Divya Bansal, Sanjeev Sofat . Privacy Preserving Techniques in Social Networks Data Publishing - A Review. International Journal of Computer Applications. 87, 15 ( February 2014), 9-14. DOI=10.5120/15282-3880

@article{ 10.5120/15282-3880,
author = { Amardeep Singh, Divya Bansal, Sanjeev Sofat },
title = { Privacy Preserving Techniques in Social Networks Data Publishing - A Review },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 15 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number15/15282-3880/ },
doi = { 10.5120/15282-3880 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:59.004000+05:30
%A Amardeep Singh
%A Divya Bansal
%A Sanjeev Sofat
%T Privacy Preserving Techniques in Social Networks Data Publishing - A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 15
%P 9-14
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Development of online social networks and publication of social network data has led to the risk of leakage of confidential information of individuals. This requires the preservation of privacy before such network data is published by service providers. Privacy in online social networks data has been of utmost concern in recent years. Hence, the research in this field is still in its early years. Several published academic studies have proposed solutions for providing privacy of tabular micro-data. But those techniques cannot be straight forwardly applied to social network data as social network is a complex graphical structure of vertices and edges. Techniques like k-anonymity, its variants, L-diversity have been applied to social network data. Integrated technique of K-anonymity & L-diversity has also been developed to secure privacy of social network data in a better way.

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Index Terms

Computer Science
Information Sciences

Keywords

Privacy models K-anonymity L-diversity t-closeness.

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