A Framework For Privacy Diagnosis And Preservation In Data Publishing
Privacy preservation in data publishing aims at the publication of data with protecting private information. Although removing direct identifier of individuals seems to protect their anonymity at first glance, private information may be revealed by joining the data to other external data. Privacy p...
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2010
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my.usm.eprints.42061 http://eprints.usm.my/42061/ A Framework For Privacy Diagnosis And Preservation In Data Publishing Mirakabad, Mohammad Reza Zare QA75.5-76.95 Electronic computers. Computer science Privacy preservation in data publishing aims at the publication of data with protecting private information. Although removing direct identifier of individuals seems to protect their anonymity at first glance, private information may be revealed by joining the data to other external data. Privacy preservation addresses this privacy issue by introducing k-anonymity and l-diversity principles. Accordingly, privacy preservation techniques, namely k-anonymization and l-diversification algorithms, transform data (for example by generalization, suppression or fragmentation) to protect identity and sensitive information of individuals respectively. 2010-04 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/42061/1/MOHAMMAD_REZA_ZARE_MIRAKABAD.pdf Mirakabad, Mohammad Reza Zare (2010) A Framework For Privacy Diagnosis And Preservation In Data Publishing. PhD thesis, Universiti Sains Malaysia. |
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QA75.5-76.95 Electronic computers. Computer science |
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QA75.5-76.95 Electronic computers. Computer science Mirakabad, Mohammad Reza Zare A Framework For Privacy Diagnosis And Preservation In Data Publishing |
description |
Privacy preservation in data publishing aims at the publication of data with protecting private information. Although removing direct identifier of individuals
seems to protect their anonymity at first glance, private information may be revealed by joining the data to other external data. Privacy preservation addresses this privacy
issue by introducing k-anonymity and l-diversity principles. Accordingly, privacy preservation techniques, namely k-anonymization and l-diversification algorithms,
transform data (for example by generalization, suppression or fragmentation) to protect identity and sensitive information of individuals respectively. |
format |
Thesis |
author |
Mirakabad, Mohammad Reza Zare |
author_facet |
Mirakabad, Mohammad Reza Zare |
author_sort |
Mirakabad, Mohammad Reza Zare |
title |
A Framework For Privacy Diagnosis And
Preservation In Data Publishing
|
title_short |
A Framework For Privacy Diagnosis And
Preservation In Data Publishing
|
title_full |
A Framework For Privacy Diagnosis And
Preservation In Data Publishing
|
title_fullStr |
A Framework For Privacy Diagnosis And
Preservation In Data Publishing
|
title_full_unstemmed |
A Framework For Privacy Diagnosis And
Preservation In Data Publishing
|
title_sort |
framework for privacy diagnosis and
preservation in data publishing |
publishDate |
2010 |
url |
http://eprints.usm.my/42061/1/MOHAMMAD_REZA_ZARE_MIRAKABAD.pdf http://eprints.usm.my/42061/ |
_version_ |
1643710399502090240 |
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13.214268 |