Detection of sensitive items in market basket database using association rule mining for privacy preserving

Abstract

Data mining is an essential technology to extract patterns or knowledge from large repositories of data. Association rules in market basket database represent the shopping behavior of customers. The association information may reveal trade secrets. It must be hidden before publishing. Association rule hiding in privacy preserving data mining hides sensitive rules containing sensitive items. In this paper, a new method is proposed to detect the sensitive items for hiding sensitive association rules. This proposed method finds the frequent item sets and generates the association rules. It employs the concept of representative association rules to detect sensitive items.