For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
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
Publication Date: 2014/04/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
data mining, privacy preserving, secure multi-party computation, orthogonal transformation, Inner product operation,
Full Text: PDF(520.7KB)>>
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.