“An Analysis on Effective, Precise and Privacy Preserving Data Mining Association Rules With Partitioning on Distributed Databases” |
Data mining techniques are used to discover hiddeninformation from large databases. Among many data mining techniques,association rule mining is receiving more attention to the researchers to findcorrelations between items or items sets efficiently. In distributed databaseenvironment, the way the data is distributed plays an important role in theproblem definition. The data may be distributed horizontally or vertically orin hybrid mode among different sites. There is an increasing demand forcomputing global association rules for the databases belongs to different sitesin a way that private data is not revealed and site owner knows the globalfindings and their individual data only. In this paper a model is proposedwhich adopts a sign based secure sum cryptography technique to find globalassociation rules with trusted party by preserving the privacy of theindividual’s data when the data is distributed horizontally among differentsites. Mining distributeddatabases is emerging as a fundamental computational problem. A common approachfor mining distributed databases is to move all of the data from each databaseto a central site and a single model is built. This approach is accurate, buttoo expensive in terms of time required. For this reason, several approacheswere developed to efficiently mine distributed databases, but they still ignorea key issue privacy. Privacy is the right of individuals or organizations tokeep their own information secret. Privacy concerns can prevent data movement data may be distributed among severalcustodians, none of which is allowed to transfer its data to another site.