Nergiz, M.E.Clifton, C.Nergiz, A.E.2016-02-082016-02-08200910414347http://hdl.handle.net/11693/22677k-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.EnglishIntegrityPrivacyProtectionRelational databaseSecurityIntegrityPrivacyProtectionRelational databaseSecurityClustering algorithmsArticle10.1109/TKDE.2008.210