Multirelational k-anonymity
Date
2009
Authors
Nergiz, M.E.
Clifton, C.
Nergiz, A.E.
Advisor
Instructor
Source Title
IEEE Transactions on Knowledge and Data Engineering
Print ISSN
10414347
Electronic ISSN
Publisher
Volume
21
Issue
8
Pages
1104 - 1117
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous data set, any identifying information occurs in at least k tuples. Much research has been done to modify a single-table data set 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. We also propose two new clustering algorithms to achieve multirelational anonymity. Experiments show the effectiveness of the approach in terms of utility and efficiency. © 2006 IEEE.
Course
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Book Title
Keywords
Integrity, Privacy, Protection, Relational database, Security, Integrity, Privacy, Protection, Relational database, Security, Clustering algorithms