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

authored by: Tek Raj Chhetri, Anelia Kurteva, Rance J. Delong, Rainer Hilscher, Kai Korte, Anna Fensel
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.
Organisation(s): Institute of Legal Informatics
External Organisation(s): University of Innsbruck
The Open Group
Research Triangle Institute International
Wageningen University and Research
Type: Article
Journal: Sensors
Volume: 22
ISSN: 1424-8220
Publication date: 03.04.2022
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Analytical Chemistry, Information Systems, Atomic and Molecular Physics, and Optics, Biochemistry, Instrumentation, Electrical and Electronic Engineering
Sustainable Development Goals: SDG 11 - Sustainable Cities and Communities
Electronic version(s): https://doi.org/10.3390/s22072763 (Access: Open)