Survey Paper on Secure Data Mining for Horizontally Distributed DataBase

Abstract

Data mining is used to extract important knowledge from large datasets, but sometimes these datasets are split
among various parties.Privacy liability may prevent the parties from directly sharing the data and some types of
information about the data.This paper presents different methods for secure mining of association rules in horizontally
distributed databases. The main aim of this paper is protocol for secure mining of association rules in horizontally
distributed databases. The current main protocol is that of Kantarcioglu and Clifton. This protocol, like theirs, is based
on the Fast Distributed Mining (FDM) algorithm of Cheung et al., which is an unsecured distributed version of the
Apriori algorithm. The main components in this 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. This protocol offers improved privacy with respect to the protocol
in. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost
and computational cost.