Paper Info
Title
Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent
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.
Year
DOI
Venue
2022
10.3390/s22072763
SENSORS
Keywords
DocType
Volume
GDPR, privacy, compliance verification, informed consent, standard data protection model, data sharing, data protection by design, knowledge graph, distributed systems
Journal
22
Issue
ISSN
Citations 
7
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Tek Raj Chhetri101.69
Anelia Kurteva212.04
Rance J DeLong300.34
Rainer Hilscher400.34
Kai Korte500.34
Anna Fensel601.35