Privacy Preserving Association Rule Mining using Horizontally Partition Data: Review Paper

Abstract

Data mining is used to extract interested pattern or knowledge from large amount of data using many data mining technique. However it may also display sensitive information about individuals compromising the individual right to privacy When a collection of data is split among various parties. Now Each and Every party would wants to keep its sensitive information private during the mining process. Privacy preserving data mining is to develop data mining method without increases the risk of misuse of data. The main aim of privacy preserving data mining is to find the global mining results by preserving the individual sites private data/information. The various methods such as randomization, perturbation, heuristic and cryptography techniques. To Find privacy pre serving association rule mining in horizontally and vertically partitioned databases. In this paper, the analysis of different methods for PPARM is performed and their results are compared. Horizontally Partitioned databases, algorithm that combines advantage of both RSA public key cryptosystem and Homomorphic encryption scheme and algorithm that uses Paillier cryptosystem to compute global supports are used. This paper reviews the wide methods used for mining association rules over horizontally distributed dataset while preserving privacy.