PARALLEL DISSEMINATED DATA WITH SECURE EXTRACTION OF ASSOCIATION RULE

Abstract: Recent developments in information technology have made possible the collection and analysis of millions of transactions containing personal data. These data include shopping habits, criminal records, medical histories and credit records among others. A protocol has been proposed for secure mining of association rules in horizontally distributed databases. This protocol is optimized than the Fast Distributed Mining (FDM) algorithm which is an unsecured distributed version of the Apriori algorithm. Overcome the problem of FDP Using fast Distributed Mining (FDM) algorithm. There are two rules, one that computes the union of private subsets that each of the interacting group of actors hold, and another that tests the inclusion of an element held by one player in a subset held by another. This paper proposed some rule that offers improved privacy with respect to the proposed rule. The main purpose of this protocol is to remove the problem of mining generalized association rules that affects the existing system. This protocol offers more enhanced privacy with respect to previous protocols. In addition it is simpler and is optimized in terms of communication rounds, communication cost and computational cost than other protocols.

Keywords: Data Mining, FDM Distributed Computation, Frequent Item sets, Association Rule, Privacy preserving.