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)


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.


Privacy Data Pattern Hidding Data Generalisation 


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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

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