Evaluation of generalization based K-anonymization algorithms

The Electronic-Era has brought the major challenge to the individual's privacy by collecting the individual's information. This information is a threat to the privacy as it is published to the third party for the purpose of either research or study. Even though the identity is not published, based on some informative attributes and publicly available data, fraudulent can access the information which is supposed to be private. As a result, many researchers are attracted towards the challenge and developed many solutions. This paper is aimed to give comparative evolution of the various generalization hierarchy based K-anonymization algorithms. Major challenge while preserving the privacy of an individual, is to keep published data useful for the further research and analysis. Also, the data generated is voluminous and it should take less amount of time for anonymization. In this work these algorithms are compared for efficiency (in terms of time) and utility loss.