Identifying the Risk of Attribute Disclosure by Mining Fuzzy Rules View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Irene Díaz , José Ranilla , Luis J. Rodríguez-Muniz , Luigi Troiano

ABSTRACT

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. More... »

PAGES

455-464

References to SciGraph publications

Book

TITLE

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods

ISBN

978-3-642-14054-9
978-3-642-14055-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-14055-6_47

DOI

http://dx.doi.org/10.1007/978-3-642-14055-6_47

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050282605


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