Securing association rule mining with FP growth algorithm in horizontally partitioned database

Data mining examines large pre-existing databases in order to generate new information. There are various tasks included under Data mining and association rule mining is considered as one of the crucial tasks among its. They are in form of if-then kind of statements which help to find relationships among huge data which do not hold relationship with each other within a relational database or any other information repository. 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 required for individual transaction and for frequent itemsets when the database is partitioned horizontally among multiple sites. In this case, every site is interested in globally supported association rules without revealing its own local information. To fulfill this goal, We use 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 are using a Frequent-Pattern (FP) growth algorithm to find frequent itemsets and try to reduce total computation time.