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An Efficient Clustering Algorithm for k-Anonymisation

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Abstract

K-anonymisation is an approach to protecting individuals from being identified from data. Good k-anonymisations should retain data utility and preserve privacy, but few methods have considered these two con°icting requirements together. In this paper, we extend our previous work on a clustering-based method for balancing data utility and privacy protection, and propose a set of heuristics to improve its effectiveness. We introduce new clustering criteria that treat utility and privacy on equal terms and propose sampling-based techniques to optimally set up its parameters. Extensive experiments show that the extended method achieves good accuracy in query answering and is able to prevent linking attacks effectively.

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References

  1. Li N, Li T, Venkatasubramanian S. t-closeness: Privacy beyond k-anonymity and l-diversity. In Proc. ICDE, Istanbul, Turkey, 2007, pp.106–115.

  2. Loukides G, Shao J. Speeding up clustering-based k-anonymisation algorithms with pre-partitioning. In Proc. The 24th British National Conference on Databases, Glasgow, UK, 2007, pp.203–214.

  3. Loukides G, Shao J. Capturing data usefulness and privacy protection in K-anonymisation. In Proc. The 22nd Annual ACM Symposium on Applied Computing, Seoul, Korea, 2007, pp.370–374.

  4. Sweeney L. K-anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10(5): 557–570.

    Article  MATH  MathSciNet  Google Scholar 

  5. Samarati P. Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering, 2001, 13(9): 1010–1027.

    Article  Google Scholar 

  6. LeFevre K, DeWitt D J, Ramakrishnan R. Mondrian multi-dimensional K-anonymity. In Proc. ICDE, Atlanta, Georgia, USA, 2006, p.25.

  7. Bayardo R J, Agrawal R. Data privacy through optimal k-anonymization. In Proc. ICDE, Tokyo, Japan, 2005, pp.217–228.

  8. Iyengar V S. Transforming data to satisfy privacy constraints. In Proc. KDD, Edmonton, Alberta, Canada, 2002, pp.279–288.

  9. LeFevre K, DeWitt D J, Ramakrishnan R. Workload-aware anonymization. In Proc. KDD, Philadelphia, PA, USA, 2006, pp.277–286.

  10. Fung B C M, Wang K, Yu P S. Top-down specialization for information and privacy preservation. In Proc. ICDE, Tokyo, Japan, 2005, pp.205–216.

  11. Teng Z, Du W. Comparisons of k-anonymization and randomization schemes under linking attacks. In Proc. ICDM, Hong Kong, China, 2006, pp.1091–1096.

  12. Machanavajjhala A Gehrke J, D Kifer et al. l-diversity: Privacy beyond k-anonymity. In Proc. ICDE, Atlanta, Georgia, USA, 2006, p.24.

  13. Koudas N, Zhang Q, Srivastava D et al. Aggregate query answering on anonymized tables. In Proc. ICDE, Istanbul, Turkey, 2007, pp.116–125.

  14. Hettich S, Merz C J. UCI Repository of machine learning databases, 1999, http://kdd.ics.uci.edu.

  15. LeFevre K, DeWitt D J, Ramakrishnan R. Incognito: Efficient full-domain K-anonymity. In Proc. SIGMOD, Baltimore, Maryland, USA, 2005, pp.49–60.

  16. Xu J, Wang W, Pei J et al. Utility-based anonymization using local recoding. In Proc. KDD, Philadelphia, PA, USA, 2006, pp.785–790.

  17. Aggarwal C C, Yu P S. A condensation approach to privacy preserving data mining. In Proc. The 9th International Conference on Extending Database Technology, Heraklion, Crete, Greece, 2004, pp.183–199.

  18. Byun J, Kamra E, Bertino E et al. Efficient k-anonymization using clustering techniques. In Proc. The 12th International Conference on Database Systems for Advanced Applications, 2007, Bangkok, Thailand, pp.188–200.

  19. Zhou J, Sander J. Data bubbles for non-vector data: Speeding-up hierarchical clustering in arbitrary metric spaces. In Proc. VLDB, Berlin, Germany, 2003, pp.452–463.

  20. Narayan B L, Murthy C A, Pal S K. Maxdiff kd-trees for data condensation. Pattern Recogn. Lett., 27(3): 187–200.

  21. Xiao X, Tao Y. Anatomy: Simple and effective privacy preservation. In Proc. VLDB, Seoul, Korea, 2006, pp.139–150.

  22. Gehrke J, Ramakrishnan R, Ganti V. RainForest — A Framework for fast decision tree construction of large datasets. In Proc. VLDB, New York City, USA, 1998, pp.416–427.

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Authors and Affiliations

  1. School of Computer Science, Cardiff University, Cardiff, U.K.

    Grigorios Loukides & Jian-Hua Shao

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  1. Grigorios Loukides
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  2. Jian-Hua Shao
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Correspondence to Grigorios Loukides.

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Loukides, G., Shao, JH. An Efficient Clustering Algorithm for k-Anonymisation. J. Comput. Sci. Technol. 23, 188–202 (2008). https://doi.org/10.1007/s11390-008-9121-3

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  • DOI: https://doi.org/10.1007/s11390-008-9121-3

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