Data Anonymization with Imprecise Rules and Its Performance Evaluations

Abstract

Privacy protection is absolutely imperative for data release while the utilization of public data and big data is getting popular. In this paper, data anonymization methods using rough set-based rule induction are investigated. It has been shown that many rules with imprecise conclusions can improve the classification accuracy of the rule-based classifier. Data anonymization methods utilizing rules with imprecise conclusions are proposed. The data tables anonymized by one the proposed methods can preserve the classification accuracy of rules induced from them. The proposed methods as well as the conventional data anonymization methods are compared from both viewpoints of the classification accuracy of rules induced from the anonymized data table and the preservation of data anonymity. The results show the usefulness of the proposed methods.