International Journal of Soft Computing and Engineering (IJSCE)
Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k-1 other records with respect to certain \"Identifying\" attributes. In this paper, the authors discuss the concept of k-anonymity, from its original proposal illustrating its enforcement via generalization and suppression. They also discuss different ways in which generalization and suppressions can be applied to satisfy k-anonymity.