Abstract

A tremendous explosion in the production and storage of data has occurred in the last few years. It is crucial to analyze massive amounts of data in order to derive information since several commercial applications generate this data. Data mining methods provide valuable insights to businesses by using a variety of technologies. Association rule mining is one of data minings major techniques it seeks to identify relationships among transactional information. Since the associations sensitive information shouldnt get out throughout the rule mining process data security is of the utmost importance. In this study we survey the various privacy-preserving association rule mining approaches. Several techniques to privacy preservation are covered including those based on heuristics exactness borders reconstruction and cryptography. In addition it provides a comprehensive analysis of several privacy-preserving association rule mining approaches and a number of performance assessment measures th