An improved EMHS algorithm for privacy preserving in association rule mining on horizontally partitioned database

Abstract

The advances of data mining techniques played an important role in many areas for various applications. In context of privacy and security issues, the problems caused by association rule mining technique are recently investigated. The misuse of this technique may disclose the database owner’s sensitive information to others. Hence, the privacy of individuals is not maintained. Many of the researchers have recently made an effort to preserve privacy of sensitive knowledge or information in a real database. In this paper, we have modified EMHS Algorithm to improve its efficiency by using Elliptic Curve Cryptography. We have used ElGamal Cryptography technique of ECC for homomorphic encryption. Analysis of the experiment on various datasets show that proposed algorithm is efficient compared to EMHS in terms of computation time.