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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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.usm.my/42061/1/MOHAMMAD_REZA_ZARE_MIRAKABAD.pdf http://eprints.usm.my/42061/ |
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| Summary: | 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. |
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