Knowledge Graph Anonymization using Semantic Anatomization.

As the usage of RDF-based Knowledge Graphs is going mainstream, it becomes necessary for organizations and companies to consider the privacy preservation of the data they are managing and possibly sharing. This is generally performed by anonymization techniques such as triple suppression and generalization. Nevertheless, these techniques have the drawback of reducing the utility of the released datasets. This paper presents semantic anatomization, a novel anonymization technique, that retains all quasi-identifier and sensitive values in the RDF graph. Due to an aggregating mechanism and the exploitation of the semantics contained in ontologies, this technique preserves data correlation and supports high quality analysis from anonymized graphs. We demonstrate the potential of semantic anatomization on large graphs generated from our own extension of the well-established Lehigh university benchmark.