A Privacy Preserving Algorithm for Mining Rare Association Rules by Homomorphic Encryption

Abstract

Privacy-preserving data mining have greate significance in the era of big data. The Privacy-preserving condition on rare association rules mining is about the sensitive information regarding participants. Each side have a private dataset, aims to collaboratively find rare association rules on data set like a logically unified frame, but actually composed of distributed private data set. We proposed a new efficient algorithm to discover privacy-preserving rare association rule mining technique. The main principle idea is that with the secure two-party computation theory we employ homomorphic encryption to hide the private information.