Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect use...

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Autores principales: Rupali Gangarde, Amit Sharma, Ambika Pawar, Rahul Joshi, Sudhanshu Gonge
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Lenguaje:EN
Publicado: MDPI AG 2021
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spelling oai:doaj.org-article:a11866db50c243bd987b0f7d2ecf763f2021-11-25T17:25:34ZPrivacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness10.3390/electronics102228772079-9292https://doaj.org/article/a11866db50c243bd987b0f7d2ecf763f2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2877https://doaj.org/toc/2079-9292As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.Rupali GangardeAmit SharmaAmbika PawarRahul JoshiSudhanshu GongeMDPI AGarticleanonymizationclusteringk-anonymityl-diversityonline social networkprivacy preservationElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2877, p 2877 (2021)
institution DOAJ
collection DOAJ
language EN
topic anonymization
clustering
k-anonymity
l-diversity
online social network
privacy preservation
Electronics
TK7800-8360
spellingShingle anonymization
clustering
k-anonymity
l-diversity
online social network
privacy preservation
Electronics
TK7800-8360
Rupali Gangarde
Amit Sharma
Ambika Pawar
Rahul Joshi
Sudhanshu Gonge
Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness
description As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.
format article
author Rupali Gangarde
Amit Sharma
Ambika Pawar
Rahul Joshi
Sudhanshu Gonge
author_facet Rupali Gangarde
Amit Sharma
Ambika Pawar
Rahul Joshi
Sudhanshu Gonge
author_sort Rupali Gangarde
title Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness
title_short Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness
title_full Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness
title_fullStr Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness
title_full_unstemmed Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness
title_sort privacy preservation in online social networks using multiple-graph-properties-based clustering to ensure k-anonymity, l-diversity, and t-closeness
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/a11866db50c243bd987b0f7d2ecf763f
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