A Rough Set Based Efficient l-diversity Algorithm

Most of the organizations publish micro data for a variety of purposes including demographic and public health research. To protect the anonymity of the entities, data holders often remove or encrypt explicit identifiers. But, released information often contains quasi identifiers, which leak valuable information. Samarati and Sweeney introduced the concept of k-anonymity to handle this problem and several algorithms have been introduced by different authors in recent times. Lin et al put forth a new clustering-based method known as OKA for k-anonymization. But, k-anonymity can create groups that leak information due to homogeneity attack. This problem is tackled by the notion of ldiversity introduced by Machanavajjhala et al. Recently, the OKA algorithm is improved by Tripathy et al by making some modifications in the adjustment stage and introducing distinct l-diversity into it. But, in most of the modern databases impreciseness has become a common characteristic, which is not handled by any of the above algorithms. The primary purpose of this paper is to use MMeR, an algorithm introduced by Tripathy et al, in developing a suitable anonymisation algorithm which is applicable to any database having precise or imprecise heterogeneous data and satisfies both k-anonymity as well as l-diversity properties.