Contributions to privacy and data protection from a multi-disciplinary model-based software and systems engineering approach
- Autores: Yod Samuel Martín García
- Directores de la Tesis: Juan Carlos Yelmo García (dir. tes.)
- Lectura: En la Universidad Politécnica de Madrid ( España ) en 2022
- Idioma: español
- Materias:
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- Resumen
Information privacy and personal data protection have gained relevance in recent years in parallel to the explosion of business models that are based on the exploiting those personal data and which are leveraged by the increase in available computing power and capacity of communications networks. Many initiatives have been developed to address these concerns from civil society, academia, and institutions, of which the enactment of stricter regulatory frameworks (and in particular the EU General Data Protection Regulation or GDPR) is a paramount example.
It is in this context that privacy engineering has emerged as a field in software and systems engineering that vouches for the introduction of methods, tools, and techniques into the engineering practice to address privacy and data protection concerns from a systematic and economical approach. This way, privacy engineering recognizes the key role that engineers have in ensuring privacy and data protection in the systems they develop, and responds to that from the body of knowledge and the accumulated wisdom of software and systems engineering, by introducing methods, tools, and techniques aligned the those employed by the engineering community of practice in their job.
In this dissertation, we present our contributions to the field of privacy and data protection engineering, leveraging the model-based software and systems engineering (MBSSE) approach, as it provides a demonstrated way to systematically organize knowledge about the system and its context, to integrate system-specific and aspect-oriented viewpoints, and to facilitate the processing of the models by both human and automated means. In particular, we have especially focused on the integration of privacy and data protection concerns into several engineering disciplines (risk management, requirements engineering, design, systems assurance, and method engineering): More specifically, this dissertation includes the following contributions: - A working definition of privacy and data protection and an adversary model suitable for privacy and data protection engineering.
- An analysis of the gap between the needs of engineers in relation to the implementation of privacy and data protection principles (and, in particular, compliance with the EU GDPR) and the support by privacy management tools, and a proposal to support these needs from the perspectives of the above-mentioned engineering disciplines.
- An implementation-independent Domain-Specific Aspect Language (DSAL) to annotate a variety of system models with privacy and data protection features.
- A requirements management methodological framework to address privacy and data protection requirements from a dual perspective (risk-based and goal-oriented) and the application of the latter to operationalize the contents of ISO/IEC 29100.
- A system for privacy design patterns, including morphological elements (structure), syntactical elements (relationships), and lexical elements (instances).
- A method for privacy and data protection assurance (especially, privacy and data protection impact assessments) including the definition of a process-oriented reference framework, argumentation patterns, and mapping between legal regulations (GDPR), technical standards (ISO/IEC 29134), and industry guidance (smart grid PIA template).
- A methodological metamodel for privacy and data protection engineering methods, the definition of a set of privacy and data protection processes to be introduced throughout the System Development Lifecycle (SDLC), their interactions between one another, and an architecture of a software toolset to support that process.
- A policy brief with recommendations for future legal and institutional developments.
All in all, these contributions set the scene for the introduction of privacy and data protection throughout the SDLC, and they have been validated in the context of EU-funded research projects (PRIPARE, TRUESSEC.EU, PDP4E), in the last of which other partners have implemented open-source software tools to support parts of the methods that we have devised and introduced in this dissertation. Besides, a community on privacy and data protection engineering by models is being established at the Eclipse Foundation to pursue our work in the future.
