Using blocking approach to preserve privacy in classification rules by inserting dummy Transaction

Abstract

The increasing rate of data sharing among organizations could maximize the risk of leaking sensitive knowledge. Trying to solve this problem leads to increase the importance of privacy preserving within the process of data sharing. In this study is focused on privacy preserving in classification rules mining as a technique of data mining. We propose a blocking algorithm to hiding sensitive classification rules. In the solution, rules' hiding occurs as a result of editing a set of transactions which satisfy sensitive classification rules. The proposed approach tries to deceive and block adversaries by inserting some dummy transactions. Finally, the solution has been evaluated and compared with other available solutions. Results show that limiting the number of attributes existing in each sensitive rule will lead to a decrease in both the number of lost rules and the production rate of ghost rules.