Anonymization Ontology: A Privacy-Preserving Framework for GDPR Compliant Anonymization of Personal Health Data

Abstract

Medical research can immensely benefit from the use of personal health data. However, a large amount of health data remains unexplored due to existing data protection laws guaranteeing individual full control of their data. For a broad use in research, this health data needs to be anonymized without losing its specific characteristics. The anonymization ensures compliance with the data protection laws while providing a wealth of data, e.g., to train machine learning algorithms. In this paper, we present an ontology to describe generic anonymization software for GDPR-compliant health data anonymization as well as its characteristics and requirements.