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.
Authors acknowledge financial support by Grant MTM2008-01519 from Ministry of Science and Innovation and Grant TIN2007-61273 from Ministry of Education and Science, Government of Spain.
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Díaz, I., Ranilla, J., Rodríguez-Muniz, L.J., Troiano, L. (2010). Identifying the Risk of Attribute Disclosure by Mining Fuzzy Rules. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_47
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DOI: https://doi.org/10.1007/978-3-642-14055-6_47
Publisher Name: Springer, Berlin, Heidelberg
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