Using classification methods to evaluate attribute disclosure risk

Jordi Nin, Javier Herranz, Vicenç Torra

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

3 Cites (Scopus)


Statistical Disclosure Control protection methods perturb the non-confidential attributes of an original dataset and publish the perturbed results along with the values of confidential attributes. Traditionally, such a method is considered to achieve a good privacy level if attackers who try to link an original record with its perturbed counterpart have a low success probability. Another opinion is lately gaining popularity: the protection methods should resist not only record re-identification attacks, but also attacks that try to guess the true value of some confidential attribute of some original record(s). This is known as attribute disclosure risk. In this paper we propose a quite simple strategy to estimate the attribute disclosure risk suffered by a protection method: using a classifier, constructed from the protected (public) dataset, to predict the attribute values of some original record. After defining this approach in detail, we describe some experiments that show the power and danger of the approach: very popular protection methods suffer from very high attribute disclosure risk values.

Idioma originalAnglès
Títol de la publicacióModeling Decisions for Artificial Intelligence - 7th International Conference, MDAI 2010, Proceedings
Nombre de pàgines10
Estat de la publicacióPublicada - 2010
Publicat externament
Esdeveniment7th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2010 - Perpignan, France
Durada: 27 d’oct. 201029 d’oct. 2010

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum6408 LNAI
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349


Conferència7th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2010


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