Paper Title
An Efficient FDM based Association Rule Mining in Vertically Distributed Databases

Data mining is the extraction of unknown patterns from humongous database. Association rule mining identifies the relationships among the attributes of the database. Mining from distributed databases is complex task as the information should not be disclosed among the databases. The distributed databases can be partitioned horizontally, vertically or hybrid which is combination of horizontal and vertical. This paper extends the association rule mining in vertically distributed databases. The protocol for this association mining is based on the Fast Distributed Mining (FDM) algorithm. The novel secure algorithm not only mines the association rules but facilitate the union and intersection. While mining the association rules, data miner authentication is ensured using Tiny Encryption Algorithm (TEA). Our protocol uses TEA, a symmetric key encryption for authentication. The scheme uses optimum number of message passes for data mining to improve the operational efficiency. Keywords- Associations, Fast Distributed Mining, Horizontal Database, Tiny Encryption Algorithm.