Mining of Association Rules in Distributed Databases

Data mining is the most fast growing area today which is used to extract important knowledge from large data collections but often these collections are divided among several parties. Association rule mining is one of the technique in data mining. Here, we propose a protocol for mining of association rules in horizontally distributed databases and protocol is based on the Fast Distributed Mining (FDM) algorithm which is an unsecured distributed version of the Apriori algorithm. The main ingredients in protocol are two novel secure multi-party algorithms one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol. In addition, it is simpler and is signicantly more efficient in terms of communication rounds, communication cost and computational cost.