Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Autoren

  • Tek Raj Chhetri
  • Anelia Kurteva
  • Rance J. Delong
  • Rainer Hilscher
  • Kai Korte
  • Anna Fensel

Organisationseinheiten

Externe Organisationen

  • Universität Innsbruck
  • The Open Group
  • Research Triangle Institute International
  • Wageningen University and Research
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Details

OriginalspracheEnglisch
Aufsatznummer2763
FachzeitschriftSensors
Jahrgang22
Ausgabenummer7
PublikationsstatusVeröffentlicht - 3 Apr. 2022

Abstract

The enforcement of the GDPR in May 2018 has led to a paradigm shift in data protection. Organizations face significant challenges, such as demonstrating compliance (or auditability) and automated compliance verification due to the complex and dynamic nature of consent, as well as the scale at which compliance verification must be performed. Furthermore, the GDPR’s promotion of data protection by design and industrial interoperability requirements has created new technical challenges, as they require significant changes in the design and implementation of systems that handle personal data. We present a scalable data protection by design tool for automated compliance verification and auditability based on informed consent that is modeled with a knowledge graph. Automated compliance verification is made possible by implementing a regulation-to-code process that translates GDPR regulations into well-defined technical and organizational measures and, ultimately, software code. We demonstrate the effectiveness of the tool in the insurance and smart cities domains. We highlight ways in which our tool can be adapted to other domains.

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Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent. / Chhetri, Tek Raj; Kurteva, Anelia; Delong, Rance J. et al.
in: Sensors, Jahrgang 22, Nr. 7, 2763, 03.04.2022.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Chhetri, T. R., Kurteva, A., Delong, R. J., Hilscher, R., Korte, K., & Fensel, A. (2022). Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent. Sensors, 22(7), Artikel 2763. https://doi.org/10.3390/s22072763
Chhetri TR, Kurteva A, Delong RJ, Hilscher R, Korte K, Fensel A. Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent. Sensors. 2022 Apr 3;22(7):2763. doi: 10.3390/s22072763
Chhetri, Tek Raj ; Kurteva, Anelia ; Delong, Rance J. et al. / Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent. in: Sensors. 2022 ; Jahrgang 22, Nr. 7.
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abstract = "The enforcement of the GDPR in May 2018 has led to a paradigm shift in data protection. Organizations face significant challenges, such as demonstrating compliance (or auditability) and automated compliance verification due to the complex and dynamic nature of consent, as well as the scale at which compliance verification must be performed. Furthermore, the GDPR{\textquoteright}s promotion of data protection by design and industrial interoperability requirements has created new technical challenges, as they require significant changes in the design and implementation of systems that handle personal data. We present a scalable data protection by design tool for automated compliance verification and auditability based on informed consent that is modeled with a knowledge graph. Automated compliance verification is made possible by implementing a regulation-to-code process that translates GDPR regulations into well-defined technical and organizational measures and, ultimately, software code. We demonstrate the effectiveness of the tool in the insurance and smart cities domains. We highlight ways in which our tool can be adapted to other domains.",
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