| Title | A Model-Based Approach to Realize Privacy and Data Protection by Design |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Pedroza, Gabriel, Muntés-Mulero, Victor, Mart\'ın, Yod Samuel, Mockly, Guillaume |
| Conference Name | 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS PW) |
| Keywords | Data models, data protection, DFD, Europe, feature extraction, GDPR, Human Behavior, Industries, MBSE., MDE, Measurement, Metrics, Model-based, personal data detection, privacy, privacy by design, Privacy Policies, pubcrawl, Regulators, Scalability |
| Abstract | Telecommunications and data are pervasive in almost each aspect of our every-day life and new concerns progressively arise as a result of stakes related to privacy and data protection [1]. Indeed, systems development becomes data-centric leading to an ecosystem where a variety of players intervene (citizens, industry, regulators) and where the policies regarding data usage and utilization are far from consensual. The new General Data Protection Regulation (GDPR) enacted by the European Commission in 2018 has introduced new provisions including principles for lawfulness, fairness, transparency, etc. thus endorsing data subjects with new rights in regards to their personal data. In this context, a growing need for approaches that conceptualize and help engineers to integrate GDPR and privacy provisions at design time becomes paramount. This paper presents a comprehensive approach to support different phases of the design process with special attention to the integration of privacy and data protection principles. Among others, it is a generic model-based approach that can be specialized according to the specifics of different application domains. |
| DOI | 10.1109/EuroSPW54576.2021.00042 |
| Citation Key | pedroza_model-based_2021 |
A Model-Based Approach to Realize Privacy and Data Protection by Design