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Auflistung nach Schlagwort "accountability"

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  • Textdokument
    Anonymization Is Dead – Long Live Privacy
    (Open Identity Summit 2019, 2019) Zibuschka, Jan; Kurowski, Sebastian; Roßnagel, Heiko; Schunck, Christian H.; Zimmermann, Christian
    Privacy is a multi-faceted, interdisciplinary concept, with varying meaning to different people and disciplines. To most researchers, anonymity ist he “holy grail” of privacy research, as it suggests that it may be possible to avoid personal information altogether. However, time and time again, anonymization has been shown to be infeasible. Even de-facto anonymity is hardly achievable using state-of-the-art cryptographic anonymization techniques. Furthermore, as there are inherent tensions between the privacy protection goals of confidentiality, availability, integrity, transparency, intervenability and unlinkability, failed attempts to achieve full anonymization may make it impossible to provide data-subjects with transparency and intervenability. This is highly problematic as such mechanisms are required by regulation such as the General Data Protection Regulation (GDPR). Therefore, we argue for a paradigm shift away from anonymization towards transparency, accountability, and intervenability.
  • Zeitschriftenartikel
    DPMF: A Modeling Framework for Data Protection by Design
    (Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 10, 2020) Sion, Laurens; Dewitte, Pierre; Van Landuyt, Dimitri; Wuyts, Kim; Valcke, Peggy; Joosen, Wouter
    Building software-intensive systems that respect the fundamental rights to privacy and data protection requires explicitly addressing data protection issues at the early development stages. Data Protection by Design (DPbD)—as coined by Article 25(1) of the General Data Protection Regulation (GDPR)—therefore calls for an iterative approach based on (i) the notion of risk to data subjects, (ii) a close collaboration between the involved stakeholders and (iii) accountable decision-making. In practice, however, the legal reasoning behind DPbD is often conducted on the basis of informal system descriptions that lack systematicity and reproducibility. This affects the quality of Data Protection Impact Assessments (DPIA)—i.e. the concrete manifestation of DPbD at the organizational level. This is a major stumbling block when it comes to conducting a comprehensive and durable assessment of the risks that takes both the legal and technical complexities into account. In this article, we present DPMF, a data protection modeling framework that allows for a comprehensive and accurate description of the data processing operations in terms of the key concepts used in the GDPR. The proposed modeling approach supports the automation of a number of legal reasonings and compliance assessments (e.g., purpose compatibility) that are commonly addressed in a DPIA exercise and this support is strongly rooted upon the system description models. The DPMF is supported in a prototype modeling tool and its practical applicability is validated in the context of a realistic e-health system for a number of complementary development scenarios.