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Achieving k-Anonymity for Associative Classification in Incremental-Data Scenarios

  • Bowonsak Seisungsittisunti
  • Juggapong Natwichai
Part of the Communications in Computer and Information Science book series (CCIS, volume 223)

Abstract

When a data mining model is to be developed, one of the most important issues is preserving the privacy of the input data. In this paper, we address the problem of data transformation to preserve the privacy with regard to a data mining technique, associative classification, in an incremental-data scenario. We propose an incremental polynomial-time algorithm to transform the data to meet a privacy standard, i.e. k-Anonymity. While the transformation can still preserve the quality to build the associative classification model. The computational complexity of the proposed incremental algorithm ranges from O(n log n) to O( Δn) depending on the characteristic of increment data. The experiments have been conducted to evaluate the proposed work comparing with a non-incremental algorithm. From the experiment result, the proposed incremental algorithm is more efficient in every problem setting.

Keywords

Execution Time Class Label Generalization Level Incremental Algorithm Order Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Fung, B.C.M., Wang, K., Yu, P.S.: Top-down specialization for information and privacy preservation. In: Proceedings of the 21st International Conference on Data Engineering, pp. 205–216. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  2. 2.
    Harnsamut, N., Natwichai, J.: A novel heuristic algorithm for privacy preserving of associative classification. In: Ho, T.-B., Zhou, Z.-H. (eds.) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 273–283. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Li, W., Han, J., Pei, J.: Cmar: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE ICDM International Conference on Data Mining, pp. 369–376. IEEE Computer Society, Washington, DC (2001)Google Scholar
  4. 4.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 80–86. AAAI Press, Menlo Park (1998)Google Scholar
  5. 5.
    Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proceedings of the Twenty-third ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 223–228. ACM, New York (2004)CrossRefGoogle Scholar
  6. 6.
    Seisungsittisunti, B., Natwichai, J.: Incremental privacy preservation for associative classification. In: Proceeding of the ACM First International Workshop on Privacy and Anonymity for Very Large Databases, PAVLAD 2009, pp. 37–44. ACM, New York (2009)CrossRefGoogle Scholar
  7. 7.
    Wang, K., Yu, P.S., Chakraborty, S.: Bottom-up generalization: A data mining solution to privacy protection. In: Proceedings of the 4th IEEE International Conference on Data Mining, pp. 249–256. IEEE Computer Society, Los Alamitos (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bowonsak Seisungsittisunti
    • 1
  • Juggapong Natwichai
    • 1
  1. 1.Computer Engineering Department, Faculty of EngineeringChiang Mai UniversityChiang MaiThailand

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