Autor según el artículo: Sánchez, D. Batet, M. Viejo, A.

Departamento: Enginyeria Informàtica i Matemàtiques

Resumen: Text sanitization is crucial to enable privacy-preserving declassification of confidential documents. Moreover, considering the advent of new information sharing technologies that enable the daily publication of thousands of textual documents, automatic and semi-automatic sanitization methods are needed. Even though several of these methods have been proposed, most of them detect and sanitize sensitive terms (e.g., people names, addresses, diseases, etc.) independently, neglecting the importance of semantic correlations. From the attacker’s perspective, semantic correlations can be exploited to disclose a sanitized term from the presence of one or several non-sanitized words. To tackle this problem, this paper presents a general-purpose method that, by taking the output of a standard sanitization mechanism, analyses, detects and proposes for sanitization those semantically correlated terms that represent a plausible disclosure risk for the already sanitized ones. Our method relies on an information-theoretic formulation of disclosure risk which is able to adapt its behavior to the criterion of the initial sanitizer. The evaluation, carried on over a collection of real documents, shows that semantic correlations represent a real privacy threat in prior sanitized documents, and that our method is able to detect them effectively. As a result, the disclosure risk of the sanitized output is significantly reduced with respect to standard sanitization mechanisms.

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 0020-0255

Página final: 123

Volumen de revista: 249

Versión del articulo depositado: info:eu-repo/semantics/submittedVersion

Enlace a la fuente original: http://www.sciencedirect.com/science/article/pii/S0020025513004799

DOI del artículo: 10.1016/j.ins.2013.06.042

Entidad: Universitat Rovira i Virgili.

Año de publicación de la revista: 2013

Página inicial: 110