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Privacy Protection by Matrix Transformation
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2009/04/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Data Mining
data mining, privacy preserving, randomization,
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Privacy preserving is indispensable in data mining. In this paper, we present a novel clustering method for distributed multi-party data sets using orthogonal transformation and data randomization techniques. Our method can not only protect privacy in face of collusion, but also achieve a higher level of accuracy compared to the existing methods.