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The Research on Privacy Preserving Data Mining

Author: YuDi
Tutor: XuXiangYang
School: Hunan University
Course: Applied Computer Technology
Keywords: Privacy Classification Mining Clustering Mining Homomorphic encryption Order-preserving encryption
CLC: TP311.13
Type: Master's thesis
Year: 2009
Downloads: 314
Quote: 1
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Abstract


With the rapid development of information technology , data sharing and exchange network behavior is becoming more and more frequent. Because data mining from the extraction of useful information in the message data , so it is as an analytical tool has been widely used. While the privacy of data mining problems have attracted the attention of the people . This paper studies distributed privacy preserving data mining algorithms . The paper 's main work is as follows : First, privacy preserving data mining algorithms summarized . Then , define the basic concepts of data mining privacy , and pointed out that the goal of privacy protection mechanisms . Second, the combination of data distribution , data modification way , and privacy protection technology point of view , the typical privacy preserving data mining algorithms made ??a comprehensive analysis and presentation . The third paper on the basis of the research , the homomorphic encryption and the encryption of isotonic combination , proposed a new classification algorithm support privacy protection . Under the premise of ensuring mining results , the algorithm to solve the ciphertext math and numeric comparison , meet the privacy needs of mining , and reduced communication and computational complexity of the algorithm . Experimental results show that : relative to similar algorithms , the algorithm is a communication complexity of the linear efficient solutions , have been greatly improved in computational efficiency . Fourth, by changing the the data vertical distribution under conditions clustering step , a clustering method based on encryption technology . This paper to apply the method to the k- centers clustering algorithm to protect data privacy while effective clustering mining . Experimental results show that : the proposed algorithm to achieve a better balance between computational overhead and communication overhead , compared with similar algorithms , the algorithm has a high operating efficiency , less computing and communication overhead , and by hiding the plaintext distribution , so all of the site can be a powerful protection to solve the problem of the site subset privacy leakage .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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