吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (01): 179-185.

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Entropy-based classification approach for personalized privacy anonymity

WANG Bo1,2, YANG Jing1, ZHANG Jian-pei1   

  1. 1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China;
    2. School of Automation, Harbin University of Science and Technology, Harbin 150080, China
  • Received:2011-11-14 Online:2013-01-01 Published:2013-01-01

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Abstract: To solve the defects of the traditional (α, k)-anonymity model has the defect that it does not satisfy the personalized requirement of different privacy preserving degree for different sensitive attributed values. To overcome this defect, the concepts of personalized privacy sensitive factor and personalized privacy preserving requirement degrees for each sensitive attribute value are introduced. Then the personalized (α, k)-anonymity model is defined formally. Meanwhile, an entropy-based classification approach for personalized privacy anonymity is presented to solve this personalized (α, k)-anonymity model. Experiment results show that the proposed method not only produces similar information loss and time cost to the existing (α, k)-anonymity algorithm, but also meets the requirements of personalized service and achieves more reasonable privacy preservation.

Key words: computer application, privacy preserving, (&alpha, , k)-anonymity, entropy classification, personalized

CLC Number: 

  • TP393
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