Details
| Original language | English |
|---|---|
| Article number | 2763 |
| Journal | Sensors |
| Volume | 22 |
| Issue number | 7 |
| Publication status | Published - 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.
Keywords
- compliance verification, data protection by design, data sharing, distributed systems, GDPR, informed consent, knowledge graph, privacy, standard data protection model
ASJC Scopus subject areas
- Chemistry(all)
- Analytical Chemistry
- Computer Science(all)
- Information Systems
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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In: Sensors, Vol. 22, No. 7, 2763, 03.04.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent
AU - Chhetri, Tek Raj
AU - Kurteva, Anelia
AU - Delong, Rance J.
AU - Hilscher, Rainer
AU - Korte, Kai
AU - Fensel, Anna
N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/4/3
Y1 - 2022/4/3
N2 - 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.
AB - 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.
KW - compliance verification
KW - data protection by design
KW - data sharing
KW - distributed systems
KW - GDPR
KW - informed consent
KW - knowledge graph
KW - privacy
KW - standard data protection model
UR - http://www.scopus.com/inward/record.url?scp=85127382516&partnerID=8YFLogxK
U2 - 10.3390/s22072763
DO - 10.3390/s22072763
M3 - Article
C2 - 35408377
AN - SCOPUS:85127382516
VL - 22
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 7
M1 - 2763
ER -