Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014 (2014), Article ID 686151, 10 pages
http://dx.doi.org/10.1155/2014/686151
Research Article

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

Web Science Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received 8 December 2013; Accepted 24 February 2014; Published 30 March 2014

Academic Editors: B. Johansson and L. Xiao

Copyright © 2014 Chongjing Sun et al. 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, “Fast algorithms for mining association rules,” in Proceedings of the 20th International Conference on Very Large Data Bases (VLDB '94), vol. 1215, pp. 487–499, Santiago de Chile, Chile, September 1994.
  2. L. F. Cranor, J. Reagle, and M. S. Ackerman, Beyond Concern: Understanding Net Users' Attitudes about Online Privacy, MIT Press, Cambridge, Mass, USA, 2000.
  3. J. R. Haritsa, “Mining association rules under privacy constraints,” in Privacy-Preserving Data Mining, pp. 239–266, Springer, New York, NY, USA, 2008. View at Google Scholar
  4. X. Xiao and Y. Tao, “Personalized privacy preservation,” in Proceedings of the ACM International Conference on Management of Data (SIGMOD '06), pp. 229–240, ACM, Chicago, Ill, USA, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Xiao, Y. Tao, and M. Chen, “Optimal random perturbation at multiple privacy levels,” Proceedings of the VLDB Endowment, vol. 2, no. 1, pp. 814–825, 2009. View at Google Scholar
  6. A. C. Tamhane, “Randomized response techniques for multiple sensitive attributes,” Journal of the American Statistical Association, vol. 76, no. 376, pp. 916–923, 1981. View at Publisher · View at Google Scholar
  7. S. J. Rizvi and J. R. Haritsa, “Maintaining data privacy in association rule mining,” in Proceedings of the 28th International Conference on Very Large Data Bases (VLDB '02), pp. 682–693, VLDB Endowment, Hong Kong, August 2002.
  8. V. S. Verykios and A. Gkoulalas-Divanis, “A survey of association rule hiding methods for privacy,” in Privacy-Preserving Data Mining, pp. 267–289, Springer, New York, NY, USA, 2008. View at Google Scholar
  9. Y.-H. Wu, C.-M. Chiang, and A. L. P. Chen, “Hiding sensitive association rules with limited side effects,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 1, pp. 29–42, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. E. D. Pontikakis, A. A. Tsitsonis, and V. S. Verykios, “An experimental study of distortion-based techniques for association rule hiding,” in Research Directions in Data and Applications Security XVIII, vol. 144 of IFIP International Federation for Information Processing, pp. 325–339, Springer, 2004. View at Publisher · View at Google Scholar
  11. E. D. Pontikakis, Y. Theodoridis, A. A. Tsitsonis, L. Chang, and V. S. Verykios, “A quantitative and qualitative analysis of blocking in association rule hiding,” in Proceedings of the ACM Workshop on Privacy in the Electronic Society (WPES '04), pp. 29–30, ACM Press, Washington, DC, USA, October 2004. View at Scopus
  12. S.-L. Wang and A. Jafari, “Using unknowns for hiding sensitive predictive association rules,” in Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI '05), pp. 223–228, IEEE, Las Vegas, Nev, USA, August 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Xingzhi and P. S. Yu, “A border-based approach for hiding sensitive frequent itemsets,” in Proceedings of the 5th IEEE International Conference on Data Mining (ICDM '05), pp. 426–433, IEEE, Houston, Tex, USA, November 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. G. V. Moustakides and V. S. Verykios, “A max-min approach for hiding frequent itemsets,” in Proceedings of the 6th IEEE International Conference on Data Mining Workshops (ICDM '06), pp. 502–506, Hong Kong, December 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Menon, S. Sarkar, and S. Mukherjee, “Maximizing accuracy of shared databases when concealing sensitive patterns,” Information Systems Research, vol. 16, no. 3, pp. 256–270, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Gkoulalas-Divanis and V. S. Verykios, “An integer programming approach for frequent itemset hiding,” in Proceedings of the 15th ACM Conference on Information and Knowledge Management (CIKM '06), pp. 748–757, ACM, Arlington, Va, USA, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Vaidya and C. Clifton, “Privacy preserving association rule mining in vertically partitioned data,” in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '02), pp. 639–644, ACM, Alberta, Canada, July 2002. View at Scopus
  18. M. Kantarcioglu and C. Clifton, “Privacy-preserving distributed mining of association rules on horizontally partitioned data,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 9, pp. 1026–1037, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. N. Zhang, S. Wang, and W. Zhao, “A new scheme on privacy preserving association rule mining,” in Knowledge Discovery in Databases: PKDD 2004, vol. 3202 of Lecture Notes in Computer Science, pp. 484–495, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. 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
  21. Z. Zheng, R. Kohavi, and L. Mason, “Real world performance of association rule algorithms,” in Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '01), pp. 401–406, ACM, San Francisco, Calif, USA, August 2001. View at Scopus