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2025-06
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info:eu-repo/semantics/openAccess
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Universidad Nacional de Educación a Distancia (UNED). E.T.S. de Ingeniería Informática
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Resumen
La proliferación de aplicaciones móviles y redes sociales ha incrementado significativamente las preocupaciones en torno a la protección de la privacidad, debido al tratamiento extensivo y, en muchos casos, opaco de datos personales. Enrespuesta a este desafío, se propone SafeMountain, un innovador marco basado en inteligencia artificial orientado a la identificación, medición y representación estructurada de riesgos de privacidad en entornos móviles y plataformas sociales, asegurando la conformidad con marcos normativos internacionales, especialmente el Reglamento General de Protección de Datos (GDPR). SafeMountain integra técnicas de análisis estático y dinámico del código para examinar el comportamiento real de las aplicaciones en relación con la gestión de la información personal, permitiendo detectar posibles vulneraciones. Además, aplica métodos avanzados de Procesamiento del Lenguaje Natural (PLN) para analizar de manera automatizada las políticas de privacidad y los Términos de Servicio, contrastando sus declaraciones con los permisos solicitados y las acciones ejecutadas por las aplicaciones para identificar inconsistencias o incumplimientos normativos. El marco introduce un sistema de evaluación objetiva del riesgo, alineado con principios internacionales y directrices regulatorias, que clasifica los riesgos en función de criterios técnicos y jurídicos. Esta evaluación contempla dimensiones clave como la previsibilidad, la capacidad de gestión y la disociabilidad del tratamiento de datos, utilizando un esquema visual de semáforo (verde, amarillo, rojo) que facilita la interpretación y comprensión por parte de los distintos actores implicados. SafeMountain cubre carencias relevantes en el estado del arte, como la falta de métricas estandarizadas y de herramientas visuales integrales para el análisis del riesgo de privacidad. Al proporcionar una visión clara sobre la coherencia entre permisos, prácticas reales y declaraciones textuales, así como sobre las brechas de seguridad y cumplimiento, el sistema permite a usuarios, desarrolladores y organizaciones
The widespread adoption of mobile applications and social networks has significantly intensified concerns regarding privacy protection, due to the extensive (and often opaque) processing of personal data. In response to this challenge, SafeMountain is introduced as an innovative artificial intelligence-based framework aimed at identifying, quantifying, and structurally representing privacy risks in mobile environments and social platforms, ensuring compliance with international regulatory standards, particularly the General Data Protection Regulation (GDPR). SafeMountain integrates static and dynamic code analysis techniques to examine the actual behavior of applications in terms of personal data handling, enabling the detection of potential privacy breaches. It also employs advanced Natural Language Processing (NLP) methods to automatically analyze privacy policies and Terms of Service, comparing their textual claims against the permissions requested and the actions performed by applications to identify inconsistencies or regulatory violations. The framework introduces an objective risk assessment system, aligned with international principles and regulatory guidelines, which classifies risks based on both technical and legal criteria. This evaluation considers key dimensions such as predictability, manageability, and the dissociability of data processing, using a traffic light visualization scheme (green, yellow, red) to facilitate interpretation and understanding by all relevant stakeholders. SafeMountain addresses important gaps in the current state of the art, such as the lack of standardized metrics and integrated visual tools for privacy risk analysis. By providing a clear view of the consistency between requested permissions, real behaviors, and textual declarations (as well as highlighting security and compliance gaps) the system enables users, developers, and organizations to take informed and preventive actions. Ultimately, SafeMountain contributes to strengthening digital trust through a proactive, transparent, and ethical approach to personal data protection.
The widespread adoption of mobile applications and social networks has significantly intensified concerns regarding privacy protection, due to the extensive (and often opaque) processing of personal data. In response to this challenge, SafeMountain is introduced as an innovative artificial intelligence-based framework aimed at identifying, quantifying, and structurally representing privacy risks in mobile environments and social platforms, ensuring compliance with international regulatory standards, particularly the General Data Protection Regulation (GDPR). SafeMountain integrates static and dynamic code analysis techniques to examine the actual behavior of applications in terms of personal data handling, enabling the detection of potential privacy breaches. It also employs advanced Natural Language Processing (NLP) methods to automatically analyze privacy policies and Terms of Service, comparing their textual claims against the permissions requested and the actions performed by applications to identify inconsistencies or regulatory violations. The framework introduces an objective risk assessment system, aligned with international principles and regulatory guidelines, which classifies risks based on both technical and legal criteria. This evaluation considers key dimensions such as predictability, manageability, and the dissociability of data processing, using a traffic light visualization scheme (green, yellow, red) to facilitate interpretation and understanding by all relevant stakeholders. SafeMountain addresses important gaps in the current state of the art, such as the lack of standardized metrics and integrated visual tools for privacy risk analysis. By providing a clear view of the consistency between requested permissions, real behaviors, and textual declarations (as well as highlighting security and compliance gaps) the system enables users, developers, and organizations to take informed and preventive actions. Ultimately, SafeMountain contributes to strengthening digital trust through a proactive, transparent, and ethical approach to personal data protection.
Descripción
Categorías UNESCO
Palabras clave
SafeMountain, Riesgo de Privacidad Automatizado, Aplicaciones Móviles, Técnicas de Inteligencia Artificial, Metodología PRISMA, Privacidad de Datos, Marco de Riesgo de Privacidad, Privacidad en Redes Sociales, SafeMountain, Automated Privacy Risk, Mobile Apps, AI Techniques, PRISMA Methodology, Data Privacy, Privacy Risk Framework, Social Networks Privacy
Citación
Blanco Aza, Daniel. Trabajo fin de Máster: Evaluación Exhaustiva de Riesgos de Privacidad Basada en IA en Aplicaciones Móviles y Redes Sociales. Universidad Nacional de Educación a Distancia (UNED) 2025
Centro
E.T.S. de Ingeniería Informática
Departamento
Sistemas de comunicación y Control