Privacy preserving association rule mining in vertically partitioned data

Abstract

Privacy concerns typically constrain data processing technique. This paper addresses the matter of association rule mining wherever transactions square measure distributed across sources. Every web site holds some attributes of every dealings and also the sites would like to collaborate to globally valid association rules. However, the sites should not reveal individual dealings knowledge. This paper addresses the matter of association rule mining wherever transactions are distributed across sources. This paper have a tendency to specialize in privacy-preserving mining on vertically divided knowledge.This paper addresses a replacement model is planned to seek out association rules by satisfying the privacy constraints for vertically divided databases at n range of web sites in conjunction with data laborer. This model adopts cryptography techniques like encryption, decoding techniques and real number technique to seek out association rules expeditiously and firmly for vertically divided databases. Keywords: Privacy Preserving Data Mining, Distributed Data Mining, Information Security, Association Rule Mining, Secure Multiparty Computation.