A k-Anonymous Rule Clustering Approach for Data Publishing

Abstract

Classification rules should be open for public inspection to ensure fairness. These rules can be originally induced from some dataset. If induced classification rules are supported only by a small number of objects in the dataset, publication can lead to identification of objects supporting the rule, given their speciality. Eventually, it is possible to retrieve information about the identified objects. This identifiability is not desirable in terms of data privacy. In this paper, to avoid such privacy breaches, we propose rule clustering for achieving k-anonymity of all induced rules, i.e., the induced rules are supported by at least k objects in the dataset. The proposed approach merges similar rules to satisfy k-anonymity while aiming to maintain the classification accuracy. Two numerical experiments were executed to verify both the accuracy of the classifier with the rules obtained by the proposed method and the ratio of decision classes revealed from leaked information about objects. The experimental results show the usefulness of the proposed method.