Clustering based Categorical Data Protection

Abstract

At present, the number of publicly available datasets is increasing day by day. It is therefore imperative to improve the confidentiality of the data, which has become one of the main reasons for an investigation. Extended to provide effective protection techniques that hinder the disclosure of entities in datasets while preserving the usefulness of the data. A systematic approach to categorical data protection is achieved by applying groups to the dataset and then protecting each group. In this paper, we present a survey and analysis on clustering techniques. The analysis of grouping techniques can result in confidential data or outliers in groups, and effective protection methods for such groups.