Interpretability of Fuzzy Association Rules as means of Discovering Threaths to Privacy (CMMSE 2010)

Luigi Troiano (1) , Luis J Rodriguez-Muñiz (2) , José Ranilla (3) , Irene Díaz (4)

1 Dipartimento di Ingegneria [Benevento]
2 Statistics and Operation Research
3 Universidad de Oviedo [Oviedo]
4 Computer Science

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.

Mots clés: Physical Sciences

Domaines: Informatique [cs]

Luigi Troiano, Luis J Rodriguez-Muñiz, José Ranilla, Irene Díaz. Interpretability of Fuzzy Association Rules as means of Discovering Threaths to Privacy (CMMSE 2010). International Journal of Computer Mathematics, 2011, pp.1. ⟨10.1080/00207160.2011.613460⟩. ⟨hal-00739203⟩