RDF Graph Anonymization Robust to Data Linkage

  • Remy DelanauxEmail author
  • Angela Bonifati
  • Marie-Christine Rousset
  • Romuald Thion
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)


Privacy is a major concern when publishing new datasets in the context of Linked Open Data (LOD). A new dataset published in the LOD is indeed exposed to privacy breaches due to the linkage to objects already present in the other datasets of the LOD. In this paper, we focus on the problem of building safe anonymizations of an RDF graph to guarantee that linking the anonymized graph with any external RDF graph will not cause privacy breaches. Given a set of privacy queries as input, we study the data-independent safety problem and the sequence of anonymization operations necessary to enforce it. We provide sufficient conditions under which an anonymization instance is safe given a set of privacy queries. Additionally, we show that our algorithms for RDF data anonymization are robust in the presence of sameAs links that can be explicit or inferred by additional knowledge.


Linked Open Data Data privacy RDF anonymization 



This work has been supported by the Auvergne-Rhône-Alpes region through the ARC6 research program funding Remy Delanaux’s PhD; by the LabEx PERSYVAL-Lab (ANR-11-LABX-0025-01); by the SIDES 3.0 project (ANR-16-DUNE-0002) funded by the French Programme Investissement d’Avenir (PIA); and by the Palse Impulsion 2016/31 program (ANR-11-IDEX-0007-02) at UDL.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Remy Delanaux
    • 1
    Email author
  • Angela Bonifati
    • 1
  • Marie-Christine Rousset
    • 2
    • 3
  • Romuald Thion
    • 1
  1. 1.Université Lyon 1, LIRIS CNRSVilleurbanneFrance
  2. 2.Université Grenoble Alpes, CNRS, INRIA, Grenoble INPGrenobleFrance
  3. 3.Institut Universitaire de FranceParisFrance

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