State-of-the-art in privacy preserved k-anonymity revisited

Abstract

The prevalent conditions in data sharing and mining have necessitated the release and revelation of certain vulnerable private information. Thus the preservation of privacy has become an eminent field o f study in data security. In addressing this issue, K anonymity is amongst the most reliable and valid algorithms used for privacy preservation in data mining. It is ubiquitously used in myriads of fields in recent years for its characteristic effective prevention ability towards the loss of vulnerable information under linking attacks. This study presents the basic notions and deep insight of the existing privacy preserved K anonymity model and its possible enhancement. Furthermore, the present challenges, excitements an d future progression of privacy preservation in K anonymity are emphasized. Moreover, this study is grounded on the fundamental ideas and concepts of the existing K anonymity privacy preservation, K anonymity model and enhanced the K anonymity model. Finally, it extracted the developmental direction of privacy preservation in K anonymity.