Privacy-Preserving Association Rule Mining Using Binary TLBO for Data Sharing in Retail Business Collaboration

Abstract

Sharing of data provides mutual benefits for collaborating organizations. Data mining techniques have allowed regimented discovery of knowledge from huge databases. Conversely, in the case of sharing the data with others, knowledge discovery raises the possibility of revealing the sensitive knowledge. The need of privacy prompted the growth of numerous privacy-preserving data mining techniques. In order to deal with privacy concerns, the database is to be transformed into another database in such a way that the sensitive knowledge is concealed. One subarea of privacy-preserving data mining, which got attention in retail businesses, is privacy-preserving association rule mining. A significant feature of privacy-preserving association rule mining is attaining a balance between privacy and precision, which is characteristically conflicting, and refining the one generally reduces the other one. In this paper, the problem has been planned in the perspective of protecting association rules which are sensitive by prudently amending the transactions of the database. To moderate the loss of non-sensitive association rules and to improve the quality of the transformed database, the proposed approach competently estimates the impact of an alteration to the database. The proposed method selects the transactions for alterations using the binary TLBO optimization technique during the concealing process. Experimental outcomes exhibit the efficiency of the proposed algorithm.