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

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Article number2763
JournalSensors
Volume22
Issue number7
DOIs
Publication statusPublished - 3 Apr 2022

Keywords

  • compliance verification
  • data protection by design
  • data sharing
  • distributed systems
  • GDPR
  • informed consent
  • knowledge graph
  • privacy
  • standard data protection model

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