Privacy preserving mining of Association Rules on horizontally and vertically partitioned data: A review paper

Abstract

Data mining can extract important knowledge from large database - sometimes this database is split among various parties. Here, the main aim of privacy preserving data mining is to find the global mining results by preserving the individual sites private data/information. Many Privacy Preserving Association Rule Mining (PPARM) algorithms are proposed for different partitioning methods by satisfying privacy constraints. The various methods such as randomization, perturbation, heuristic and cryptography techniques are proposed by different authors to find privacy preserving 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. For satisfying the privacy constraints in vertically partitioned databases, algorithm based on cryptography techniques, Homomorphic encryption, Secure Scalar product and Shamir's secret sharing technique are used. For horizontal 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 distributed dataset while preserving privacy.