A novel privacy preserving decision tree induction

Data mining algorithms extract knowledge from very large data. In most cases the large data has to be shared among multiple users where security plays a vital role. This paper deals with an efficient privacy preserving decision tree construction by reducing communication and computation cost while performing secure cardinality of scalar product during tree induction. The algorithm scales well with more number of parties.