Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng

Preserving privacy of users has been one of the important research issues in social networks. Social networks contain sensitive personal information that are often released for business and research purposes. The privacy of a user can be breached if the data are not released in an anonymized form. I...

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Main Author: Chong , Kah Meng
Format: Thesis
Published: 2020
Subjects:
Online Access:http://studentsrepo.um.edu.my/13223/
http://studentsrepo.um.edu.my/13223/1/Chong_Kah_Meng.pdf
http://studentsrepo.um.edu.my/13223/2/Chong_Kah_Meng.pdf
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author Chong , Kah Meng
author_facet Chong , Kah Meng
author_sort Chong , Kah Meng
building UM Research Repository
collection Online Access
description Preserving privacy of users has been one of the important research issues in social networks. Social networks contain sensitive personal information that are often released for business and research purposes. The privacy of a user can be breached if the data are not released in an anonymized form. In this thesis, we address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. To counter these privacy risks while preserving high utility of the published data, we define two key privacy properties, namely edge weight unlinkability and node unlinkability. We design two novel anonymization schemes namely MinSwap and
first_indexed 2025-11-14T14:04:17Z
format Thesis
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institution University Malaya
institution_category Local University
last_indexed 2025-11-14T14:04:17Z
publishDate 2020
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spelling um-132232022-04-26T22:46:16Z Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng Chong , Kah Meng Q Science (General) QA Mathematics Preserving privacy of users has been one of the important research issues in social networks. Social networks contain sensitive personal information that are often released for business and research purposes. The privacy of a user can be breached if the data are not released in an anonymized form. In this thesis, we address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. To counter these privacy risks while preserving high utility of the published data, we define two key privacy properties, namely edge weight unlinkability and node unlinkability. We design two novel anonymization schemes namely MinSwap and 2020-11 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/13223/1/Chong_Kah_Meng.pdf application/pdf http://studentsrepo.um.edu.my/13223/2/Chong_Kah_Meng.pdf Chong , Kah Meng (2020) Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng. Masters thesis, Universiti Malaya. http://studentsrepo.um.edu.my/13223/
spellingShingle Q Science (General)
QA Mathematics
Chong , Kah Meng
Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng
title Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng
title_full Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng
title_fullStr Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng
title_full_unstemmed Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng
title_short Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng
title_sort secure unlinkability schemes for privacy preserving data publishing in weighted social networks / chong kah meng
topic Q Science (General)
QA Mathematics
url http://studentsrepo.um.edu.my/13223/
http://studentsrepo.um.edu.my/13223/1/Chong_Kah_Meng.pdf
http://studentsrepo.um.edu.my/13223/2/Chong_Kah_Meng.pdf