Advertisement

Generating Microdata with P-Sensitive K-Anonymity Property

  • Traian Marius Truta
  • Alina Campan
  • Paul Meyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4721)

Abstract

Existing privacy regulations together with large amounts of available data have created a huge interest in data privacy research. A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model have been fixed by new privacy models such as p-sensitive k-anonymity, l-diversity, α, k-anonymity, and t-closeness. In this paper we introduce the EnhancedPKClustering algorithm for generating p-sensitive k-anonymous microdata based on frequency distribution of sensitive attribute values. The p-sensitive k-anonymity model and its enhancement, extended p-sensitive k-anonymity, are described, their properties are presented, and two diversity measures are introduced. Our experiments have shown that the proposed algorithm improves several cost measures over existing algorithms.

Keywords

Privacy k-anonymity p-sensitive k-anonymity attribute disclosure 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Anonymizing Tables. In: Proceedings of the ICDT, pp. 246–258 (2005)Google Scholar
  2. 2.
    Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Hippocratic Databases. In: Proceedings of the VLDB, pp. 143–154 (2002)Google Scholar
  3. 3.
    Bayardo, R.J, Agrawal, R.: Data Privacy through Optimal k-Anonymization. In: Proceedings of the IEEE ICDE, pp. 217–228. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  4. 4.
    Byun, J.W., Kamra, A., Bertino, E., Li, N.: Efficient k-Anonymity using Clustering Technique. CERIAS Tech. Report 2006-10 (2006)Google Scholar
  5. 5.
    Campan, A., Truta, T.M.: Extended P-Sensitive K-Anonymity. Studia Universitatis Babes-Bolyai Informatica 51(2), 19–30 (2006)zbMATHGoogle Scholar
  6. 6.
    Campan, A., Truta, T.M., Miller, J., Sinca, R.A.: Clustering Approach for Achieving Data Privacy. In: Proceedings of the International Data Mining Conference (2007)Google Scholar
  7. 7.
    HIPAA.: Health Insurance Portability and Accountability Act (2002), Available online at: http://www.hhs.gov/ocr/hipaa
  8. 8.
    ICD9.: International Classification of Diseases. Available online at: http://icd9cm.chrisendres.com/index.php
  9. 9.
    Iyengar, V.: Transforming Data to Satisfy Privacy Constraints. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 279–288. ACM Press, New York (2002)Google Scholar
  10. 10.
    Lambert, D.: Measures of Disclosure Risk and Harm. Journal of Official Statistics 9, 313–331 (1993)Google Scholar
  11. 11.
    LeFevre, K., DeWitt, D., Ramakrishnan, R.: Incognito: Efficient Full-Domain K-Anonymity. In: Proceedings of the ACM SIGMOD, pp. 49–60. ACM Press, New York (2005)Google Scholar
  12. 12.
    LeFevre, K., DeWitt, D., Ramakrishnan, R.: Mondrian Multidimensional K-Anonymity. In: Proceedings of the IEEE ICDE, vol. 25 (2006)Google Scholar
  13. 13.
    Li, N., Li, T., Venkatasubramanian, S.: T-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: Proceedings of the IEEE ICDE (2007)Google Scholar
  14. 14.
    Machanavajjhala, A., Gehrke, J., Kifer, D.: L-Diversity: Privacy beyond K-Anonymity. In: Proceedings of the IEEE ICDE, vol. 24 (2006)Google Scholar
  15. 15.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, UC Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  16. 16.
    Samarati, P.: Protecting Respondents Identities in Microdata Release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)CrossRefGoogle Scholar
  17. 17.
    Sweeney, L.: k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems 10(5), 557–570 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Sweeney, L.: Achieving k-Anonymity Privacy Protection Using Generalization and Suppression. International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems 10(5), 571–588 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Truta, T.M., Bindu, V.: Privacy Protection: P-Sensitive K-Anonymity Property. In: Proceedings of the Workshop on Privacy Data Management, In Conjunction with IEEE ICDE, vol. 94 (2006)Google Scholar
  20. 20.
    Truta, T.M., Campan, A.: K-Anonymization Incremental Maintenance and Optimization Techniques. In: Proceedings of the ACM SAC, pp. 380–387. ACM Press, New York (2007)Google Scholar
  21. 21.
    Winkler, W.: Matching and Record Linkage. In: Business Survey Methods, Wiley, Chichester (1995)Google Scholar
  22. 22.
    Wong, R.C-W., Li, J., Fu, A.W-C., Wang, K.: (α, k)-Anonymity: An Enhanced k-Anonymity Model for Privacy-Preserving Data Publishing. In: Proceedings of the ACM KDD, pp. 754–759. ACM Press, New York (2006)Google Scholar
  23. 23.
    Xiao, X., Tao, Y.: Personalized Privacy Preservation. In: Proceedings of the ACM SIGMOD, pp. 229–240. ACM Press, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Traian Marius Truta
    • 1
  • Alina Campan
    • 2
  • Paul Meyer
    • 1
  1. 1.Department of Computer Science, Northern Kentucky University, Highland Heights, KY 41099USA
  2. 2.Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, RO-400084Romania

Personalised recommendations