  These days, investigations of information are becoming essential for various
associations all over the globe. By and large, different associations need to
perform information examinations on their joined data sets. Privacy and
security have become a relentless concern wherein business experts do not
desire to contribute their classified transaction data. Therefore, there is a
requirement to build a proficient methodology that can process the broad
mixture of data and convert those data into meaningful knowledge for the user
without forfeiting the security and privacy of individuals crude information.
We devised two unique protocols for frequent mining itemsets in horizontally
partitioned datasets while maintaining privacy. In such a scenario, data
possessors outwork mining tasks on their multiparty data by preserving privacy.
The proposed framework model encompasses two or more data possessors who
encrypt their information and dispense their encrypted data to two or more
clouds by a data share allocator algorithm. This methodology protects the data
possessor raw data from other data possessors and the other clouds. To
guarantee data privacy, we plan a proficient enhanced homomorphic encryption
conspire. Our approach ensures privacy during communication and accumulation of
data and guarantees no information or data adversity and no incidental
consequences for data utility.
