Privacy Preserved Mining of Association Rules Over Horizontally Partitioned Data

Abstract

In this paper, we survey the basic fundamental paradigms and notions of secure multiparty computation to the field of privacy-preserving data mining. Additionally reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. The main ingredients in the protocol are two new secure multi-party algorithms one who reckon the particular abutment connected with non-public subsets that every one of the interacting parties cling on and another that test the admittance of an element placed by one particular party in a very subset placed by another. We tend to analyze the performance of secure implementations of the efficient protocols.

Keywords: Data mining; multi party computation; association rules and privacy