Advertisement

Data Mining pp 489-501 | Cite as

Data Security, Privacy and Data Mining

  • Krzysztof J. Cios
  • Roman W. Swiniarski
  • Witold Pedrycz
  • Lukasz A. Kurgan

Keywords

Data Mining Fuzzy Cluster Information Granule Partition Matrix Proximity Matrix 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, R., and Srikant, R. 2000. Privacy-preserving data mining. In Proceedings. of the ACM SIGMOD Conference on Management of Data, ACM Press, 439–450Google Scholar
  2. 2.
    Claerhout, B., and DeMoor, G.J.E. 2005. Privacy protection for clinical and genomic data: The use of privacy-enhancing techniques in medicine. International Journal of Medical Informatics, 74(2–4): 257–265CrossRefGoogle Scholar
  3. 3.
    Clifton, C. 2000. Using sample size to limit exposure to data mining. Journal of Computer Security 8(4): 281–307Google Scholar
  4. 4.
    Clifton, C., and Marks, D. 1996. Security and privacy implications of data mining. In Workshop on Data Mining and Knowledge Discovery, 15–19, Montreal, CanadaGoogle Scholar
  5. 5.
    Clifton, C., and Thuraisingham, B. 2001. Emerging standards for data mining. Computer Standards & Interfaces, 23(3): 187–193CrossRefGoogle Scholar
  6. 6.
    Da Silva, J.C., Giannella, C., Bhargava, R., Kargupta, H., and Klusch, M. 2005. Distributed data mining and agents, Engineering Applications of Artificial Intelligence, 18(7): 791–807CrossRefGoogle Scholar
  7. 7.
    Du, W., and Zhan, Z. 2002. Building decision tree classifier on private data. In Clifton, C., Estivill-Castro, V. (Eds.), IEEE ICDM Workshop on Privacy, Security and Data Mining, Conferences in Research and Practice in Information Technology, 14, 1–8, Maebashi City, Japan, ACSGoogle Scholar
  8. 8.
    Evfimievski, A., Srikant, R., Agrawal, R., and Gehrke, J. 2004. Privacy preserving mining of association rules, Information Systems, 29(4): 343–364CrossRefGoogle Scholar
  9. 9.
    Johnsten, T., and Raghavan, V.V. 2002. A methodology for hiding knowledge in databases. In Clifton, C., Estivill-Castro, C. (Eds.), IEEE ICDM Workshop on Privacy, Security and Data Mining, Conferences in Research and Practice in Information Technology, 14, 9–17, Maebashi City, Japan, ACSGoogle Scholar
  10. 10.
    Kargupta, H., Kun, L., Datta, S., Ryan, J., and Sivakumar, K. 2003. Homeland security and privacy sensitive data mining from multi-party distributed resources, Proceedings 12th IEEE International Conference on Fuzzy Systems, FUZZ ’03, 2: 1257–1260Google Scholar
  11. 11.
    Lindell, Y., and Pinkas, B. 2000. Privacy preserving data mining. In Lecture Notes in Computer Science, 1880: 36–54Google Scholar
  12. 12.
    Merugu, S., and Ghosh, J. 2005. A privacy-sensitive approach to distributed clustering, Pattern Recognition Letters, 26(4): 399–410Google Scholar
  13. 13.
    Park, B., and Kargupta, H. 2003. Distributed data mining: algorithms, systems, and applications. In Ye, N. (Ed.), The Handbook of Data Mining, Lawrence Erlbaum Associates, 341–358Google Scholar
  14. 14.
    Pedrycz, W. 2005. Knowledge-Based Clustering: From Data to Information Granules, John Wiley, Hoboken, NJGoogle Scholar
  15. 15.
    Pedrycz, W. 2002. Collaborative fuzzy clustering, Pattern Recognition Letters, 23(14): 1675–1686Google Scholar
  16. 16.
    Pinkas, B. 2002. Cryptographic techniques for privacy-preserving data mining. ACM SIGKDD Explorations Newsletter, 4(2): 12–19Google Scholar
  17. 17.
    Strehl, A., and Ghosh, J. 2002. Cluster ensembles—a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3: 583–617Google Scholar
  18. 18.
    Tsoumakas, G., Angelis, L., and Vlahavas, I. 2004. Clustering classifiers for knowledge discovery from physically distributed databases, Data & Knowledge Engineering, 49(3): 223–242Google Scholar
  19. 19.
    Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., and Theodoridis, Y. 2004. State-of-the-art in privacy preserving data mining. SIGMOD Record, 33(1): 50–57Google Scholar
  20. 20.
    Wang, S.L., and Jafari, A. 2005. Using unknowns for hiding sensitive predictive association rules. Proceedings 2005 IEEE International Conference on Information Reuse and Integration, 223–228Google Scholar
  21. 21.
    Wang, E.T., Lee, G., and Lin, Y.T. 2005. A novel method for protecting sensitive knowledge in association rules mining, Proceedings of the 29th Annual International Computer Software and Applications Conference (COMPSAC 2005), 2: 511–516Google Scholar
  22. 22.
    Wang, K., Yu, P.S., and Chakraborty, S. 2004. Bottom-up generalization: a data mining solution to privacy protection, Proceedings of the 4th IEEE International Conference on Data Mining, ICDM 2004, 249–256Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Krzysztof J. Cios
    • 1
    • 2
  • Roman W. Swiniarski
    • 3
  • Witold Pedrycz
    • 4
  • Lukasz A. Kurgan
    • 5
  1. 1.Virginia Commonwealth University Computer Science DeptRichmond
  2. 2.University of ColoradoUSA
  3. 3.Computer Science DeptSan Diego State University & Polish Academy of SciencesSan DiegoUSA
  4. 4.Electrical and Computer Engineering DeptUniversity of AlbertaEdmontonCanada
  5. 5.Electrical and Computer Engineering DeptUniversity of AlbertaEdmontonCanada

Personalised recommendations