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An Efficient Local-Recoding k-Anonymization Algorithm Based on Clusterin

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Transactions on Edutainment XI

Part of the book series: Lecture Notes in Computer Science ((TEDUTAIN,volume 8971))

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Abstract

KACA is a typical local-recoding k-anonymization algorithm. It can generate k-anonymizing data with high quality. The main drawback of KACA algorithm is its high computational cost in dealing with large dataset. To remedy this problem, we propose an new efficient k-anonymization algorithm. The main idea of the proposed algorithm is that we first adopt the c-modes algorithm to partition the whole dataset into some large clusters, and then take KACA algorithm to k-anonymize each cluster separately. Finally, comprehensive experiments demonstrate the effectiveness of our algorithm.

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Acknowledgements

This paper is supported by the major science and technology projects of Shaoxing city (2010A21034).

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Authors and Affiliations

  1. Computer Branch, Zhejiang Industry Polytechnic College, Shaoxing, 321000, China

    Lifeng Yu & Qiong Yang

Authors
  1. Lifeng Yu
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  2. Qiong Yang
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Corresponding author

Correspondence to Lifeng Yu .

Editor information

Editors and Affiliations

  1. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  2. Professor of Pervasive Computing, City University London, London, United Kingdom

    Adrian David Cheok

  3. University of Education Weingarten, Weingarten, Germany

    Wolfgang Mueller

  4. Zejiang University, Hangzhou, China

    Mingmin Zhang

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Yu, L., Yang, Q. (2015). An Efficient Local-Recoding k-Anonymization Algorithm Based on Clusterin. In: Pan, Z., Cheok, A., Mueller, W., Zhang, M. (eds) Transactions on Edutainment XI. Lecture Notes in Computer Science(), vol 8971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48247-6_24

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  • DOI: https://doi.org/10.1007/978-3-662-48247-6_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48246-9

  • Online ISBN: 978-3-662-48247-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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