Query-based Linked Data Anonymization

Rémy Delanaux 1 Angela Bonifati 1, 2 Marie-Christine Rousset 3, 4 Romuald Thion 1
1 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 TYREX - Types and Reasoning for the Web
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : We introduce and develop a declarative framework for privacy-preserving Linked Data publishing in which privacy and utility policies are specified as SPARQL queries. Our approach is data-independent and leads to inspect only the privacy and utility policies in order to determine the sequence of anonymization operations applicable to any graph instance for satisfying the policies. We prove the soundness of our algorithms and gauge their performance through experiments.
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Rémy Delanaux, Angela Bonifati, Marie-Christine Rousset, Romuald Thion. Query-based Linked Data Anonymization. The 17th International Semantic Web Conference (ISWC 2018), Oct 2018, Monterey, United States. pp.530-546. ⟨hal-01896276⟩

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