Multirelational k-Anonymity

Authors:
Mehmet Ercan Nergiz;Christopher Clifton;Ahmet Erhan Nergiz
Affiliations:
Sabanci University, Istanbul, Purdue University, West Lafayette;Purdue University, West Lafayette;Bilkent University, Istanbul, Purdue University, West Lafayette
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2009

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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.