Privacy Preserving Data Mining Services on the Web

  • Ayça Azgın Hintoğlu
  • Yücel Saygın
  • Salima Benbernou
  • Mohand Said Hacid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3592)

Abstract

Data mining research deals with extracting useful information from large collections of data. Since data mining is a complex process that requires expertise, it is beneficial to provide it as a service on the web. On the other hand, such use of data mining services combined with data collection efforts by private and government organizations leads to increased privacy concerns. In this work, we address the issue of preserving privacy while providing data mining services on the web and present an architecture for privacy preserving sharing of data mining models on the web. In the proposed architecture, data providers use APPEL for specifying their privacy preferences on data mining models, while data collectors use P3P policies for specifying their data-usage practices. Both parties use PMML as the standard for specifying data mining queries, constraints and models.

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References

  1. 1.
    Agrawal, R., Srikant, R.: Privacy Preserving Data Mining. In: ACM SIGMOD Conference on Management of Data, Dallas, Texas, pp. 439–450 (May 2000)Google Scholar
  2. 2.
    Sarawagi, S., Nagaralu, S.H.: Data mining models as services on the Internet. In: SIGKDD Explorations, pp. 24–28. ACM Press, New York (2000)Google Scholar
  3. 3.
    Verykios, V.S., Elmagarmid, A., Bertino, E., Saygin, Y., Dasseni, E.: Association Rule Hiding. IEEE TKDE 16(4) (2004)Google Scholar
  4. 4.
    Oliviera, S.R.M., Zaïne, O.R.: Foundations for an Access Control Model for Privacy Preservation in Multi-Relational Association Rule Mining. In: Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, Maebashi City, Japan, December 01, pp. 19–26 (2002)Google Scholar
  5. 5.
    Oliveira, S.R.M., Zaïane, O.R., Saygın, Y.: Secure association rule sharing. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 74–85. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Hacigumus, H., Iyer, B., Li, C., Mehrotra, S.: Executing SQL over Encrypted Data in the Database-Service-Provider Model. In: SIGMOD 2002 (2002)Google Scholar
  7. 7.
    Predictive Model Markup Language (PMML). Data Mining Group. See, http://www.dmg.org
  8. 8.
    The Platform for Privacy Preferences. See, http://www.w3.org/TR/P3P/
  9. 9.
    Cranor, L., Langheinrich, M., Marchiori, M., Presler-Marshall, M., Reagle, J.: The Platform for Privacy Preferences 1.0 (P3P1.0) Specification. W3C Recommendation (April 2002)Google Scholar
  10. 10.
    Cranor, L., Langheinrich, M., Marchiori, M.: A P3P Preference Exchange Language 1.0 (APPEL1.0). W3C Working Draft (April 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ayça Azgın Hintoğlu
    • 1
  • Yücel Saygın
    • 1
  • Salima Benbernou
    • 2
  • Mohand Said Hacid
    • 2
  1. 1.Faculty of Engineering and Natural Sciences, TuzlaSabancı UniversityIstanbulTurkey
  2. 2.LIRIS – Lyon Research Center for Images and Intelligent Information SystemsLyon 1 UniversityVilleurbanneFrance

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