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PURE: A Privacy Aware Rule-Based Framework over Knowledge Graphs

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Database and Expert Systems Applications (DEXA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11706))

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Abstract

Open data initiatives and FAIR data principles have encouraged the publication of large volumes of data, encoding knowledge relevant for the advance of science and technology. However, to mine knowledge, it is usually required the processing of data collected from sources regulated by diverse access and privacy policies. We address the problem of enforcing data privacy and access regulations (EDPR) and propose PURE, a framework able to solve this problem during query processing. PURE relies on the local as view approach for defining the rules that represent the access control policies imposed over a federation of RDF knowledge graphs. Moreover, PURE maps the problem of checking if a query meets the privacy regulations to the problem of query rewriting (QRP) using views; it resorts to state-of-the-art QRP solutions for determining if a query violates or not the defined policies. We have evaluated the efficiency of PURE over the Berlin SPARQL Benchmark (BSBM). Observed results suggest that PURE is able to scale up to complex scenarios where a large number of rules represents diverse types of policies.

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Acknowledgement

This work has been partially supported by the EU H2020 RIA funded project iASiS with grant agreement No. 727658.

Author information

Authors and Affiliations

  1. Universidad Simón Bolívar, Caracas, Venezuela

    Marlene Goncalves & Maria-Esther Vidal

  2. TIB Leibniz Information Centre for Science and Technology, Hanover, Germany

    Maria-Esther Vidal & Kemele M. Endris

  3. L3S Research Center, Hanover, Germany

    Maria-Esther Vidal & Kemele M. Endris

Authors
  1. Marlene Goncalves
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  2. Maria-Esther Vidal
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  3. Kemele M. Endris
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Corresponding author

Correspondence to Marlene Goncalves .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  4. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  5. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  6. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Goncalves, M., Vidal, ME., Endris, K.M. (2019). PURE: A Privacy Aware Rule-Based Framework over Knowledge Graphs. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-27615-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27614-0

  • Online ISBN: 978-3-030-27615-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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