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A Privacy-Preserving Classification Mining Algorithm

  • Weiping Ge
  • Wei Wang
  • Xiaorong Li
  • Baile Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3518)

Abstract

Privacy-preserving classification mining is one of the fast-growing sub-areas of data mining. How to perturb original data and then build a decision tree based on perturbed data is the key research challenge. By applying transition probability matrix this paper proposes a novel privacy-preserving classification mining algorithm which suits all data types, arbitrary probability distribution of original data, and perturbing all attributes (including label attribute). Experimental results demonstrate that decision tree built using this algorithm on perturbed data has comparable classifying accuracy to decision tree built using un-privacy-preserving algorithm on original data.

Keywords

Transition Probability Matrix Split Point Support Count Average Classification Accuracy Split Attribute 
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.
    Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, Dallas, Texas, May 2000, pp. 439–450 (2000)Google Scholar
  2. 2.
    Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Agrawal, D., Aggarwal, C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the 20th Symposium on Principles of Database Systems, Santa Barbara, California, USA (May 2001)Google Scholar
  4. 4.
    Du, W.L., Zhan, Z.J.: Using Randomized Response Techniques for Privacy-Preserving Data Mining. In: Proceedings of the 9th ACM SIGKDD Conference on Knowledge Discovery in Databases and Data Mining, Washington, DC, USA, August 24–27 (2003)Google Scholar
  5. 5.
    Agrawal, R., Ghost, S., Imielinski, T., Iyer, B., Swami, A.: An interval Classifier for database mining applications. In: Proc. of the VLDB Conference, Vancouver, British Columbia, Canada, August 1992, pp. 560–573 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Weiping Ge
    • 1
  • Wei Wang
    • 1
  • Xiaorong Li
    • 1
  • Baile Shi
    • 1
  1. 1.Department of Computing and Information TechnologyFudan UniversityShanghaiChina

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