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Multirelational k-Anonymity

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3 Author(s)
Nergiz, M.E. ; Fac. of Eng. & Natural Sci., Sabanci Univ., Istanbul, Turkey ; Clifton, C. ; Nergiz, A.E.

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.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:21 ,  Issue: 8 )

Date of Publication:

Aug. 2009

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