Table of Contents
Journal of Computational Engineering
Volume 2013 (2013), Article ID 397096, 6 pages
http://dx.doi.org/10.1155/2013/397096
Research Article

Random Response Forest for Privacy-Preserving Classification

Department of Telecommunications and Media Informatics, BME, Hungary and Inter-University Centre for Telecommunications and Informatics, Kassai str, Debrecen 4028, Hungary

Received 23 April 2013; Revised 25 September 2013; Accepted 3 October 2013

Academic Editor: André Nicolet

Copyright © 2013 Gábor Szűcs. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Agrawal and R. Srikant, “Privacy-preserving data mining,” in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD '00), vol. 29, pp. 439–450, ACM, New York, NY, USA, May 2000.
  2. B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu, “Privacy-preserving data publishing: a survey of recent developments,” ACM Computing Surveys, vol. 42, no. 4, article 14, pp. 1–53, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. X. Qi and M. Zong, “An overview of privacy preserving data mining,” Procedia Environmental Sciences, vol. 12, pp. 1341–1347, 2012. View at Publisher · View at Google Scholar
  4. V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, and Y. Theodoridis, “State-of-the-art in privacy preserving data mining,” SIGMOD Record, vol. 33, no. 1, pp. 50–57, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. C. C. Aggarwal and P. S. Yu, Privacy-Preserving Data Mining: Models and Algorithms, vol. 34 of The Kluwer International Series on Advances in Database Systems, Springer, New York, NY, USA, 2008. View at Publisher · View at Google Scholar
  6. N. Zhang, W. Zhao, and J. Chen, “Performance measurements for privacy preserving data mining,” in Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD '05), T. B. Ho, D. Cheung, and H. Liu, Eds., vol. 3518 of Lecture Notes in Computer Science, pp. 43–49, Hanoi, Vietnam, May 2005.
  7. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. S. L. Warner, “Randomized response: a survey technique for eliminating evasive answer bias,” Journal of the American Statistical Association, vol. 60, no. 309, pp. 63–66, 1965. View at Google Scholar · View at Scopus
  9. W. Du and Z. Zhan, “Using randomized response techniques for privacy-preserving data mining,” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '03), L. Getoor, T. Senator, P. Domingos, and C. Faloutsos, Eds., pp. 505–510, ACM, New York, NY, USA, August 2003.
  10. X. Xiao and Y. Tao, “Personalized privacy preservation,” in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD '06), pp. 229–240, ACM, New York, NY, USA, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Skowron and C. Rauszer, Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory, vol. 11, Springer, 1992.
  12. R. J. Bayardo and R. Agrawal, “Data privacy through optimal k-anonymization,” in Proceedings of the 21st IEEE International Conference on Data Engineering (ICDE '05), pp. 217–228, Tokoyo, Japan, April 2005. View at Scopus
  13. K. LeFevre, D. J. Dewitt, and R. Ramakrishnan, “Mondrian multidimensional k-anonymity,” in Proceedings of the 22nd IEEE International Conference on Data Engineering (ICDE '06), p. 25, Atlanta, Ga, USA, April 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. B. C. M. Fung, K. Wang, and P. S. Yu, “Anonymizing classification data for privacy preservation,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 5, pp. 711–725, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Bylander, “Estimating generalization error on two-class datasets using out-of-bag estimates,” Machine Learning, vol. 48, no. 1–3, pp. 287–297, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. Census Income Data Set, UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.