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

Content Provider	IEEE Xplore Digital Library
Author	Teo, S.G. Lee, V. Shuguo Han
Copyright Year	2012
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).
Starting Page	85
Ending Page	90
File Size	364928
Page Count	6
File Format	PDF
ISBN	9781467308670
DOI	10.1109/WAINA.2012.90
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-26
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Protocols Accuracy Secure Multiparty Computation Vectors Encryption Data mining Computational complexity secure building block privacy-preserving data mining algorithm
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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