A Contrary Recurrent Item Set Hierarchy in PPDDM

Abstract

Distributed data mining technology has become an eloquent tool for recognizing patterns and designating ideas from large pools of data procured from different sections (multiple parties). The primary focus of this research is to formulate association rules that are accepted universally, restraining the information shared about each party. A privacy-preserving algorithm is projected to mine association rules from horizontally partitioned data through a new Inverse Frequent Item set Tree. This tree was formulated using inverse frequent items and propagated only with the collaboration parties from whom the data was to be merged. A central third party was utilized in order to mine the association rules with infrequent item sets. The proposed approach constitutes a guided assistance in preserving corporate privacy, furthered by experimenting (analyzing) with the large data sets from the UCI machine learning repository. The resultant output from the approach showed positivity of preserving data with a high degree of security in multiparty computation.