Advertisement

Towards a Framework for Privacy Preserving Medical Data Mining Based on Standard Medical Classifications

  • Aurélien Faravelon
  • Christine Verdier
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 69)

Abstract

Privacy-preserving data mining often focuses on data alteration but may bias data patterns interpretation and does not offer different levels of access to patterns according to their use. This paper addresses data mining as a prediction tool and proposes to offer several levels of access to data patterns according to users’ trustworthiness. The grounding intuition is that patterns’ predictive value depends on their precision that should thus vary according to their use. The following problem is considered: a medical data holder wants to disclose data or data patterns and still control the meaning of the disclosed patterns or of the patterns that may be mined out of the released dataset. To tackle this issue, we propose a framework compliant with existing data mining techniques by modeling trust in terms of data precision and generalising data according to standard medical classifications.

Keywords

Privacy Data Pattern Hidding Data Generalisation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, Santiago de Chile (1994)Google Scholar
  2. 2.
    Dasseni, E., Verykios, V.S., Elmagarmid, A.K., Bertino, E.: Hiding association rules by using confidence and support. In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, p. 369. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Elfangary, L.M., Atteya, W.A.: Mining databases by means of an incremental asso- ciation rule learner. In: Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology, pp. 891–896. IEEE Computer Society, Washington, DC, USA (2008)CrossRefGoogle Scholar
  4. 4.
    Fayyad, U.M.: Knowledge discovery in databases: An overview. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 3–16. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    Freund, J., Comaniciu, D., Ioannis, Y., Liu, P., McClatchey, R., Morley-Fletcher, E., Pennec, X., Pongiglione, G., Zhou, X.: Health-e-child: An integrated biomedical platform for grid-based paediatric applications. CoRR abs/cs/0603036 (2006) Google Scholar
  6. 6.
    Hosmer, L.T.: Trust: The connecting link between organizational theory and philosophical ethics. The Academy of Management Review 20(2), 379–403 (1995)MathSciNetGoogle Scholar
  7. 7.
    Kim, D.J., Ferrin, D.L., Rao, H.R.: A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decis. Support Syst. 44(2), 544–564 (2008)CrossRefGoogle Scholar
  8. 8.
    Kosko, B.: Fuzzy cognitive maps. International Journal of Man-Machine Studies 24(1), 65–75 (1986)CrossRefzbMATHGoogle Scholar
  9. 9.
    Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications, pp. 108–115. IEEE Computer Society, Washington, DC, USA (2008)CrossRefGoogle Scholar
  10. 10.
    Sweeney, L., Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems 10 (2002) Google Scholar
  11. 11.
    Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 557–570 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Wang, K.: Bottom-up generalization: a data mining solution to privacy protection. In. In: ICDM, pp. 249–256 (2004)Google Scholar
  13. 13.
    Wang, S.L., Lai, T.Z., Hong, T.P., Wu, Y.L.: Hiding predictive association rules on horizontally distributed data. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 133–141. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2011

Authors and Affiliations

  • Aurélien Faravelon
    • 1
    • 2
  • Christine Verdier
    • 1
  1. 1.Laboratoire d’Informatique de GrenobleBâtiment IMAGSaint Martin d’HèresFrance
  2. 2.Groupe de recherche Philosophie, Langage & CognitionBâtiment ARSH2 Domaine universitaireGrenoble Cedex 9France

Personalised recommendations