Nergiz, Mehmet Ercan and Clifton, Christopher and Nergiz, Ahmet Erhan (2009) Multirelational k-Anonymity. IEEE Transactions On Knowledge and Data Engineering, 21 (8). pp. 1104-1117. ISSN 1041-4347
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Official URL: http://dx.doi.org/10.1109/TKDE.2008.210
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.
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