Privacy Preserving and Secure Mining of Association Rules in Distributed Data Base

Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. Local Site also finds a centre site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database. The problem of computing efficient anonymizations of partitioned databases. Given a database that is partitioned between several sites, either horizontally or vertically, we devise secure distributed algorithms that allow the different sites to obtain a k-anonymized and ?-diverse view of the union of their databases, without disclosing sensitive information. Without leaking any information about their inputs except that revealed by the algorithm’s output. Working in the standard secure multi-party computation paradigm, we present new algorithms for privacy-preserving computation of APSD (all pairs shortest distance) and SSSD (single source shortest distance), as well as two new algorithms for privacy-preserving set union. We prove that our algorithms are secure provided the participants are “honest, but curious.”