Identifying the Risk of Attribute Disclosure by Mining Fuzzy Rules

  • Irene Díaz
  • José Ranilla
  • Luis J. Rodríguez-Muniz
  • Luigi Troiano
Part of the Communications in Computer and Information Science book series (CCIS, volume 80)


In this paper we address the problem of controlling the disclosure of sensible information by inferring them by the other attributes made public. This threat to privacy is commonly known as prediction or attribute disclosure. Our approach is based on identifying those rules able to link sensitive information to the other attributes being released. In particular, the method presented in this paper is based on mining fuzzy rules. The fuzzy approach is compared to (crisp) decision trees in order to highlight pros and cons of it.


Support Vector Machine Fuzzy Rule Sensitive Information Privacy Protection Information Disclosure 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Irene Díaz
    • 1
  • José Ranilla
    • 1
  • Luis J. Rodríguez-Muniz
    • 2
  • Luigi Troiano
    • 3
  1. 1.Department of Computer ScienceUniversity of OviedoGijónSpain
  2. 2.Department of Statistics and O.R.University of OviedoGijónSpain
  3. 3.Department of EngineeringUniversity of SannioBeneventoItaly

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