Privacy preserving association rule mining by introducing concept of impact factor

Abstract

Association Rules discovered by association rule mining may contain some sensitive rules, which may cause potential threats towards privacy and security. Many of the researchers in this area have recently made efforts to preserve privacy for sensitive association rules in statistical database. In this paper, we propose a heuristic based association rule hiding using oracle real application clusters by introducing the concept of impact factor of transaction on the rule. The impact factor of a transaction is equal to number of itemsets that are present in those itemsets which represents sensitive association rule. Higher the impact factor of a transaction, higher is its sensitivity. Proposed algorithm exhibits the concept of impact factor to hide several rules by modifying fewer transactions. As modifications are fewer, data quality is very less affected. Use of clustering aids in increasing performance by running operations in parallel. © 2012 IEEE.