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Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002473/ https://www.ncbi.nlm.nih.gov/pubmed/35408377 http://dx.doi.org/10.3390/s22072763 |
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author | Chhetri, Tek Raj Kurteva, Anelia DeLong, Rance J. Hilscher, Rainer Korte, Kai Fensel, Anna |
author_facet | Chhetri, Tek Raj Kurteva, Anelia DeLong, Rance J. Hilscher, Rainer Korte, Kai Fensel, Anna |
author_sort | Chhetri, Tek Raj |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9002473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90024732022-04-13 Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent Chhetri, Tek Raj Kurteva, Anelia DeLong, Rance J. Hilscher, Rainer Korte, Kai Fensel, Anna Sensors (Basel) Article 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. MDPI 2022-04-03 /pmc/articles/PMC9002473/ /pubmed/35408377 http://dx.doi.org/10.3390/s22072763 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chhetri, Tek Raj Kurteva, Anelia DeLong, Rance J. Hilscher, Rainer Korte, Kai Fensel, Anna Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent |
title | Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent |
title_full | Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent |
title_fullStr | Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent |
title_full_unstemmed | Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent |
title_short | Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent |
title_sort | data protection by design tool for automated gdpr compliance verification based on semantically modeled informed consent |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002473/ https://www.ncbi.nlm.nih.gov/pubmed/35408377 http://dx.doi.org/10.3390/s22072763 |
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