Data privacy preservation algorithm with k-anonymity

Abstract With growing concern of data privacy violations, preservation processes become more intense. The k -anonymity method, a widely applied technique, transforms the such that publishing datasets must have at least tuples to same link-able attribute, quasi-identifiers, values. From observations, we found that, in certain domain, all quasi-identifiers datasets, can type. This type attribute is considered as an Identical Generalization Hierarchy ( IGH ) data. An has particular set characteristics could utilize for enhancing efficiency heuristic algorithms. In this paper, propose algorithm on developed from observations anonymous property problem structure eliminate constraints consideration. experiment results are presented proposed effectively preserve and also reduce number visited nodes ensuring protection, which most time-consuming process, compared efficient existing by 21%.