Privacy Preserving Decomposable Mining Association Rules on Distributed Data

Abstract

In distributed computing, data sharing is inevitable, however, moving local databases from one site to another should be avoided because of the computational overhead and privacy consideration. Most of the data mining algorithms are designed assuming that data repository is stored locally. This paper presents a scheme and algorithms for mining association rules in geographically distributed data. The proposed scheme preserves data privacy of the different geographical site by passing secure messages between them. The algorithms minimize the communication cost by exchanging statistical summaries of the local databases. We provide a privacy and security analysis that shows the privacy preserving aspects of the proposed algorithms. Moreover, the paper presents extensive simulation experiments to evaluate the efficiency of the proposed scheme.