Multirelational k-anonymity
Date
2007-04Source Title
Proceedings - International Conference on Data Engineering
Publisher
IEEE
Pages
1417 - 1421
Language
English
Type
Conference PaperItem Usage Stats
159
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233
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Abstract
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy, or overly reduce the utility of the data, in a multiple relation setting. A new clustering algorithm is proposed to achieve multirelational anonymity. © 2007 IEEE.
Keywords
Clustering algorithmsConstraint theory
Data structures
Database systems
Anonymity constraints
Multirelational anonymity
Data privacy