Security in association rule mining using secure sum technique with FP growth algorithm in horizontally partitioned database

Data mining is used to examine large and preexisting databases to formulate new information. Association rule mining is one of the important task among the various tasks of data mining. Association rules are in form of conditional statements that is used to find association between the objects which do not cotain any relation with each other. As there are many applications like market basket analysis, detection of fraud in web, medical diagnosis, census data, Customer Relationship Management of credit card business which uses association rules so it is possible to improve the process of Decision making. Security is an essential aspect for individual transaction and frequent itemsets for database partitioned horizontally. In this case, every site is interested in globally supported association rules without revealing its own local information. In this paper, We have implemented a secure multi-party algorithm based on secure sum technique to simplify the operation of mining association rule when the database is horizontally partitioned among multiple sites. We have implemented a Frequent-Pattern (FP) growth algorithm to find frequent itemsets and reduced the total computation time compared to existing system.