Fully homomorphic encryption based two-party association rule mining
Association rule mining (ARM) is one of the popular data mining methods that discover interesting correlations amongst a large collection of data, which appears incomprehensible. This is known to be a trivial task when the data is owned by one party. But when multiple data sites collectively engage in ARM, privacy concerns are introduced. Due to this concern, privacy preserving data mining algorithms have been developed to attain the desired result, while maintaining privacy. In the case of two party privacy preserving ARM for horizontally partitioned databases, both parties are required to compare their itemset counts securely. This problem is comparable to the famous millionaire problem of Yao. However, in this paper, we propose a secure comparison technique using fully homomorphic encryption scheme that provides a similar level of security to the Yao based solution, but promotes greater efficiency due to the reuse of resources.
homomorphic encryption association rules data mining privacy preservation security