Issue No. 08 - August (2009 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.210
Mehmet Ercan Nergiz , Sabanci University, Istanbul, Purdue University, West Lafayette
Christopher Clifton , Purdue University, West Lafayette
Ahmet Erhan Nergiz , Bilkent University, Istanbul, Purdue University, West Lafayette
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.
Privacy, relational database, security, integrity, protection.
A. E. Nergiz, M. E. Nergiz and C. Clifton, "Multirelational k-Anonymity," in IEEE Transactions on Knowledge & Data Engineering, vol. 21, no. , pp. 1104-1117, 2008.