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Clustering-Based k-Anonymity

  • Xianmang He
  • HuaHui Chen
  • Yefang Chen
  • Yihong Dong
  • Peng Wang
  • Zhenhua Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)

Abstract

Privacy is one of major concerns when data containing sensitive information needs to be released for ad hoc analysis, which has attracted wide research interest on privacy-preserving data publishing in the past few years. One approach of strategy to anonymize data is generalization. In a typical generalization approach, tuples in a table was first divided into many QI (quasi-identifier)-groups such that the size of each QI-group is no less than k. Clustering is to partition the tuples into many clusters such that the points within a cluster are more similar to each other than points in different clusters. The two methods share a common feature: distribute the tuples into many small groups. Motivated by this observation, we propose a clustering-based k-anonymity algorithm, which achieves k-anonymity through clustering. Extensive experiments on real data sets are also conducted, showing that the utility has been improved by our approach.

Keywords

privacy preservation algorithm proximity privacy 

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References

  1. 1.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Samarati, P.: Protecting respondents’ identities in microdata release. TKDE 13(6), 1010–1027 (2001)Google Scholar
  3. 3.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information. In: PODS 1998, p. 188. ACM, New York (1998)CrossRefGoogle Scholar
  4. 4.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations, Berkeley, pp. 281–297 (1967)Google Scholar
  5. 5.
    Kalnis, P., Ghinita, G., Mouratidis, K., Papadias, D.: Preventing location-based identity inference in anonymous spatial queries. TKDE 19(12), 1719–1733 (2007)Google Scholar
  6. 6.
    Mokbel, M.F., Chow, C.-Y., Aref, W.G.: The new casper: query processing for location services without compromising privacy. In: VLDB 2006, pp. 763–774 (2006)Google Scholar
  7. 7.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. In: ICDE 2006, p. 24 (2006)Google Scholar
  8. 8.
    Li, N., Li, T.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: KDD 2007, pp. 106–115 (2007)Google Scholar
  9. 9.
    Xu, J., Wang, W., Pei, J., Wang, X., Shi, B., Fu, A.W.-C.: Utility-based anonymization using local recoding. In: KDD 2006, pp. 785–790. ACM (2006)Google Scholar
  10. 10.
    Ghinita, G., Karras, P., Kalnis, P., Mamoulis, N.: Fast data anonymization with low information loss. In: VLDB 2007, pp. 758–769. VLDB Endowment (2007)Google Scholar
  11. 11.
    Fung, B.C.M., Wang, K., Yu, P.S.: Top-down specialization for information and privacy preservation, pp. 205–216 (2005)Google Scholar
  12. 12.
    LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Workload-aware anonymization. In: KDD 2006, pp. 277–286. ACM, New York (2006)CrossRefGoogle Scholar
  13. 13.
    Wong, W.K., Mamoulis, N., Cheung, D.W.L.: Non-homogeneous generalization in privacy preserving data publishing. In: SIGMOD 2010, pp. 747–758. ACM, New York (2010)Google Scholar
  14. 14.
    LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. In: SIGMOD 2005, pp. 49–60. ACM, New York (2005)CrossRefGoogle Scholar
  15. 15.
    Iwuchukwu, T., Naughton, J.F.: K-anonymization as spatial indexing: toward scalable and incremental anonymization. In: VLDB 2007, pp. 746–757 (2007)Google Scholar
  16. 16.
    LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Mondrian multidimensional k-anonymity. In: ICDE 2006, Washington, DC, USA, p. 25 (2006)Google Scholar
  17. 17.
    Gionis, A., Mazza, A., Tassa, T.: k-anonymization revisited. In: ICDE 2008, pp. 744–753. IEEE Computer Society, Washington, DC (2008)Google Scholar
  18. 18.
    Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: ICDE 2005, pp. 217–228. IEEE Computer Society, Washington, DC (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xianmang He
    • 1
  • HuaHui Chen
    • 1
  • Yefang Chen
    • 1
  • Yihong Dong
    • 1
  • Peng Wang
    • 2
  • Zhenhua Huang
    • 3
  1. 1.School of Information Science and TechnologyNingBo UniversityNing BoP.R. China
  2. 2.School of Computer Science and TechnologyFudan UniversityShanghaiP.R. China
  3. 3.School of Electronic and Information EngineeringTongji UniversityShanghaiP.R. China

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