Nergiz, M. ErcanClifton, C.Nergiz, A. Erhan2016-02-082016-02-082007-04http://hdl.handle.net/11693/27095Date of Conference: 15-20 April 2007Conference name: IEEE 23rd International Conference on Data Engineering, 2007k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset 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. A new clustering algorithm is proposed to achieve multirelational anonymity. © 2007 IEEE.EnglishClustering algorithmsConstraint theoryData structuresDatabase systemsAnonymity constraintsMultirelational anonymityData privacyConference Paper10.1109/ICDE.2007.369025