A Study of Efficiency and Accuracy of Secure Multiparty Protocol in Privacy-Preserving Data Mining

Abstract

An analysis of the accuracy and efficiency of multiparty secured protocols is carried out so that both measures can be optimally exploited in the design of malicious party and semi-honest party. Finding efficient protocols of the Secure Multiparty Computation(SMC) is one active research area in the field of privacy preserving data mining (PPDM). The efficiency of privacy preserving data mining is crucial to many times-sensitive applications. In this paper, we study various efficient fundamental secure building blocks such as Fast Secure Matrix Multiplication(FSMP), Secure Scalar Product (SSP), and Secure Inverse of Matrix Sum (SIMS). They are supportively embedded the enhanced features into conventional data mining. We evaluate time/space efficiency on the different protocols. Experimental results are shown that there is a trade-off of accuracy and efficiency in the secured multiparty protocols targeted on semi honest party PPDM. It is therefore articulated that dimensionality reduction techniques such as Fisher Discriminant, Graph, Lapalician, and Support Vector Machine, should be used to preprocess the data. Key contributions of this paper include, besides providing some analyses of accuracy and efficiency, are commendation on further directions for computational efficiency improvement for multiparty online real data PPDM in cloud computing platforms (private and public).