Affiliations: [a] Department of Information Management, National University of Kaohsiung, Kaohsiung 81148 Taiwan | [b] Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan | [c] Institute of Information Management, I-Shou University, Kaohsiung 84001, Taiwan
Abstract: The study of privacy preserving data mining has become more important in recent years due to the increasing amount of personal data in public, the increasing sophistication of data mining algorithms to leverage this information, and the increasing concern of privacy breaches. Association rule hiding in which some of the association rules are suppressed in order to preserve privacy has been identified as a practical privacy preserving application [5,9,12,16,19-21,23,25,28-31]. Most current association rule hiding techniques assume that the data to be sanitized are in one single data set. However, in the real world, data may exist in distributed environment and owned by non-trusting parties that might be willing to collaborate. In this work, we propose a framework to hide collaborative recommendation association rules where the data sets are horizontally partitioned and owned by non-trusting parties. Algorithms to hide the collaborative recommendation association rules and to merge the sanitized data sets are introduced. Performance and various side effects of the proposed approach are analyzed numerically. Comparisons with trusting-third-party approach are reported. The proposed non-trusting-third-party approach shows better processing time, with similar side effects.
Keywords: Privacy preserving, data mining, collaborative recommendation, association rule, horizontally partitioned
