Autor según el artículo: Sánchez D; Viejo A; Batet M

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Batet Sanromà, Montserrat / Sánchez Ruenes, David / Viejo Galicia, Luis Alexandre

Palabras clave: Privacy policies Privacy goals Privacy assessment Machine learning Gdpr

Resumen: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. To comply with the EU General Data Protection Regulation (GDPR), companies managing personal data have been forced to review their privacy policies. However, privacy policies will not solve any problems as long as users do not read or are not able to understand them. In order to assist users in both issues, we present a system that automatically assesses privacy policies. Our proposal quantifies the degree of policy compliance with respect to the data protection goals stated by the GPDR and presents clear and intuitive privacy scores to the user. In this way, users will become immediately aware of the risks associated with the services and their severity; this will empower them to take informed decisions when accepting (or not) the terms of a service. We leverage manual annotations and machine learning to train a model that automatically tags privacy policies according to their compliance (or not) with the data protection goals of the GDPR. In contrast with related works, we define clear annotation criteria consistent with the GDPR, and this enables us not only to provide aggregated scores, but also fine-grained ratings that help to understand the reasons of the assessment. The latter is aligned with the concept of explainable artificial intelligence. We have applied our method to the policies of 10 well-known internet services. Our scores are sound and consistent with the results reported in related works.

Áreas temáticas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física

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

Direcció de correo del autor: montserrat.batet@urv.cat alexandre.viejo@urv.cat david.sanchez@urv.cat

Identificador del autor: 0000-0001-8174-7592 0000-0003-2342-5100 0000-0001-7275-7887

Fecha de alta del registro: 2023-02-23

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

Enlace a la fuente original: https://www.mdpi.com/2076-3417/11/4/1762

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Applied Sciences-Basel. 11 (4): 1-11

Referencia de l'ítem segons les normes APA: Sánchez D; Viejo A; Batet M (2021). Automatic assessment of privacy policies under the gdpr. Applied Sciences-Basel, 11(4), 1-11. DOI: 10.3390/app11041762

DOI del artículo: 10.3390/app11041762

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2021

Tipo de publicación: Journal Publications