Author, as appears in the article.: Batet, Montserrat; Sanchez, David;
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Batet Sanromà, Montserrat / Sánchez Ruenes, David
Keywords: Semantics Privacy Personal data protection Knowledge
Abstract: © 2018, Emerald Publishing Limited. Purpose: To overcome the limitations of purely statistical approaches to data protection, the purpose of this paper is to propose Semantic Disclosure Control (SeDC): an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data. Design/methodology/approach: The need for data protection mechanisms able to manage data from a semantic perspective is discussed and the limitations of statistical approaches are highlighted. Then, SeDC is presented by detailing how it can be enforced to detect and protect sensitive data. Findings: So far, data privacy has been tackled from a statistical perspective; that is, available solutions focus just on the distribution of the data values. This contrasts with the semantic way by which humans understand and manage (sensitive) data. As a result, current solutions present limitations both in preventing disclosure risks and in preserving the semantics (utility) of the protected data. Practical implications: SeDC captures more general, realistic and intuitive notions of privacy and information disclosure than purely statistical methods. As a result, it is better suited to protect heterogenous and unstructured data, which are the most common in current data release scenarios. Moreover, SeDC preserves the semantics of the protected data better than statistical approaches, which is crucial when using protected data for research. Social implications: Individuals are increasingly aware of the privacy threats that the uncontrolled collection and exploitation of their personal data may produce. In this respect, SeDC offers an intuitive notion of privacy protection that users can easily understand. It also naturally captures the (non-quantitative) privacy notions stated in current legislations on personal data protection. Originality/value: On the contrary to statistical approaches to data protection, SeDC assesses disclosure risks and enforces data protection from a semantic perspective. As a result, it offers more general, intuitive, robust and utility-preserving protection of data, regardless their type and structure.
Thematic Areas: Library and information sciences Information systems Information science & library science Información y documentación Comunicació i informació Comunicação e informação Computer science, information systems Computer science applications Ciencias sociales Ciência da computação Administração pública e de empresas, ciências contábeis e turismo
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: montserrat.batet@urv.cat david.sanchez@urv.cat
Author identifier: 0000-0001-8174-7592 0000-0001-7275-7887
Record's date: 2024-09-07
Papper version: info:eu-repo/semantics/acceptedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Online Information Review. 42 (3): 290-303
APA: Batet, Montserrat; Sanchez, David; (2018). Semantic disclosure control: semantics meets data privacy. Online Information Review, 42(3), 290-303. DOI: 10.1108/OIR-03-2017-0090
Entity: Universitat Rovira i Virgili
Journal publication year: 2018
Publication Type: Journal Publications
Repositori URV