A New Hybrid Approach for Privacy Preserving Distributed Data Mining

Chongjing SUN  Hui GAO  Junlin ZHOU  Yan FU  Li SHE  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.4   pp.876-883
Publication Date: 2014/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.876
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
data mining,  privacy preserving,  secure multi-party computation,  orthogonal transformation,  Inner product operation,  

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With the distributed data mining technique having been widely used in a variety of fields, the privacy preserving issue of sensitive data has attracted more and more attention in recent years. Our major concern over privacy preserving in distributed data mining is the accuracy of the data mining results while privacy preserving is ensured. Corresponding to the horizontally partitioned data, this paper presents a new hybrid algorithm for privacy preserving distributed data mining. The main idea of the algorithm is to combine the method of random orthogonal matrix transformation with the proposed secure multi-party protocol of matrix product to achieve zero loss of accuracy in most data mining implementations.