Privacy-enhanced system design modeling based on privacy features

Ahmadian, Amir Shayan; Strüber, Daniel; Jürjens, Jan

doi:10.1145/3297280.3297431

Cited by 10 publications

(12 citation statements)

References 20 publications

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“…Finalmente, en Ahmadian et al (2019) se propone un método para modelar sistemas que mejoran la privacidad basado en elementos llamados características de privacidad (privacy features) que incluyen estrategias y subestrategias de diseño, los patrones de privacidad y las PETS (Privacy enhancing technologytecnologías para el fortalecimiento de la privacidad). En este proceso, se determina un conjunto de características de privacidad que luego se integra en el modelo inicial de un sistema para producir una versión respetuosa con la privacidad.…”

Section: Rq1 ¿Qué Metodologías De Diseño Respetuosas Con Launclassified

Elementos Reusables para Experimentar con Metodologías Basadas en Estrategias y Patrones de Privacidad

Caiza

López

Guamán

2023

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En línea con el avance global, la región latinoamericana ha establecido legislaciones que buscan establecer orden en un contexto donde la explotación de los datos personales y la protección de la privacidad de las personas son de gran relevancia. Uno de los mecanismos establecidos para alcanzar tal orden es la Protección de Datos desde el Diseño de los sistemas. Para materializarlo, los investigadores en el dominio han enfocado muchos esfuerzos en los patrones de privacidad, en metodologías basadas en ellos y en las estrategias de diseño. Sin embargo, el estado del arte señala que hace falta evidencia empírica para validar su aplicación y utilidad. Asimismo, señala la complejidad para llevar a cabo experimentos que provean dicha evidencia. El objetivo de este trabajo es contribuir a la obtención de evidencia empírica y realizar un análisis de las metodologías que hacen uso de patrones de privacidad y de estrategias de diseño para definir un conjunto de elementos que puedan ser reusados al realizar experimentos. Para ello, hemos revisado la literatura, siguiendo un conjunto de pasos sistematizados y un proceso de bola de nieve, en busca de metodologías basadas en patrones y estrategias de diseño. A partir de los resultados se observa que las estrategias de diseño de Hoepman son las más aceptadas y que hay tres enfoques de metodologías que hacen uso de ellas. En estos enfoques, se identifican elementos que podrían ser reusables al plantear experimentos: estrategias, subestrategias, patrones de privacidad, PETs, relaciones y sistemas de patrones.

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Section: Rq1 ¿Qué Metodologías De Diseño Respetuosas Con Launclassified

Elementos Reusables para Experimentar con Metodologías Basadas en Estrategias y Patrones de Privacidad

Caiza

López

Guamán

2023

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“…However, LINDDUN is missing assessment steps from a legal perspective and also is not integrated with a risk assessment process [21]. The work in [28] presents a methodology, firstly introduced in [24], to reinforce the privacy enhancement of a system design model, since there is a luck of a common a methodology concerning the design of IT systems [24]. More precisely, a systematic model-based cost estimation methodology is proposed that takes into consideration a range of privacy controls, including privacy-design strategies, patterns, and PETs as well as the interrelations and dependencies among them.…”

Section: Understanding the Differences Between Pias And The Gdpr's Dpiasmentioning

confidence: 99%

A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

et al. 2021

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As the upsurge of information and communication technologies has become the foundation of all modern application domains, fueled by the unprecedented amount of data being processed and exchanged, besides security concerns, there are also pressing privacy considerations that come into play. Compounding this issue, there is currently a documented gap between the cybersecurity and privacy risk assessment (RA) avenues, which are treated as distinct management processes and capitalise on rather rigid and make-like approaches. In this paper, we aim to combine the best of both worlds by proposing the APSIA (Automated Privacy and Security Impact Assessment) methodology, which stands for Automated Privacy and Security Impact Assessment. APSIA is powered by the use of interdependency graph models and data processing flows used to create a digital reflection of the cyber-physical environment of an organisation. Along with this model, we present a novel and extensible privacy risk scoring system for quantifying the privacy impact triggered by the identified vulnerabilities of the ICT infrastructure of an organisation. We provide a prototype implementation and demonstrate its applicability and efficacy through a specific case study in the context of a heavily regulated sector (i.e., assistive healthcare domain) where strict security and privacy considerations are not only expected but mandated so as to better showcase the beneficial characteristics of APSIA. Our approach can complement any existing security-based RA tool and provide the means to conduct an enhanced, dynamic and generic assessment as an integral part of an iterative and unified risk assessment process on-the-fly. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that such holistic security and privacy mechanisms can reach their full potential towards solving this conundrum.

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“…In contrast, approaches for enforcing data-usage policies [19,62] focus on the enforcement mechanism, making the policy statement implementation dependent and hence not a design artifact. Finally, there are approaches that do not propose any technical means for achieving privacyby-design, but rather report on experiences [39], analyze regulations [6], or exclusively work with system design models [5].…”

Section: Introductionmentioning

confidence: 99%

Model-driven Privacy

Krstic,

Nguyen,

Basin

2024

PoPETs

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Data protection regulations in many countries require IT systems to implement baseline privacy requirements like purpose limitation and consent as mandated by the GDPR. Such requirements are often specified in the system’s privacy policy and are challenging to implement as system developers must address them consistently and in a cross-cutting manner. Moreover, without a formal connection between a system’s privacy policy and its implementation, the system’s correctness and evolution are extremely difficult to attain. We propose a model-driven development methodology that incorporates privacy policies into the system design. Namely, we define a system’s privacy model, which has precise semantics and is used to specify privacy policies. We provide semantic-preserving model transformations that generate system implementations that enforce the given privacy policies by design. We implement two such model transformations, targeting C# and Python system implementations. We evaluate our methodology on three substantial case studies and show the enforcement of privacy policies related to purpose limitation and consent. Our evaluation also demonstrates our approach’s generality, effectiveness, and modest overhead.

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Privacy-enhanced system design modeling based on privacy features

Cited by 10 publications

References 20 publications

Elementos Reusables para Experimentar con Metodologías Basadas en Estrategias y Patrones de Privacidad

Elementos Reusables para Experimentar con Metodologías Basadas en Estrategias y Patrones de Privacidad

A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

Model-driven Privacy

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