Advertisement

Query-Based Linked Data Anonymization

  • Remy DelanauxEmail author
  • Angela Bonifati
  • Marie-Christine Rousset
  • Romuald Thion
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)

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.

Notes

Acknowledgements

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

References

  1. 1.
    Baader, F., Borchmann, D., Nuradiansyah, A.: Preliminary results on the identity problem in description logic ontologies. In: Description Logics. CEUR Workshop Proceedings, vol. 1879. CEUR-WS.org (2017)Google Scholar
  2. 2.
    Bagan, G., Bonifati, A., Ciucanu, R., Fletcher, G.H.L., Lemay, A., Advokaat, N.: gMark: schema-driven generation of graphs and queries. IEEE Trans. Knowl. Data Eng. 29(4), 856–869 (2017)CrossRefGoogle Scholar
  3. 3.
    Bonifati, A., Martens, W., Timm, T.: An analytical study of large SPARQL query logs. PVLDB 11(2), 149–161 (2017)Google Scholar
  4. 4.
    Bursztyn, D., Goadoué, F., Manolescu, I.: Reformulation-based query answering in RDF: alternatives and performance. PVLDB 8 (2015)CrossRefGoogle Scholar
  5. 5.
    Calvanese, D., Giacomo, G.D., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. J. Autom. Reason. 39(3), 385–429 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006).  https://doi.org/10.1007/11787006_1CrossRefGoogle Scholar
  7. 7.
    Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. 42(4), 14:1–14:53 (2010)CrossRefGoogle Scholar
  8. 8.
    Grau, B.C., Kostylev, E.V.: Logical foundations of privacy-preserving publishing of linked data. In: AAAI, pp. 943–949. AAAI Press (2016)Google Scholar
  9. 9.
    Hansen, P., Lutz, C., Seylan, I., Wolter, F.: Efficient query rewriting in the description logic el and beyond. In: IJCAI (2015)Google Scholar
  10. 10.
    Heitmann, B., Hermsen, F., Decker, S.: k – RDF-neighbourhood anonymity: combining structural and attribute-based anonymisation for linked data. In: PrivOn@ISWC. CEUR Workshop Proceedings, vol. 1951. CEUR-WS.org (2017)Google Scholar
  11. 11.
    Kirrane, S., Mileo, A., Decker, S.: Access control and the resource description framework: a survey. Semant. Web 8(2), 311–352 (2017)CrossRefGoogle Scholar
  12. 12.
    Kirrane, S., Villata, S., d’Aquin, M.: Privacy, security and policies: a review of problems and solutions with semantic web technologies. Semant. Web J. 9(2), 153–161 (2018)CrossRefGoogle Scholar
  13. 13.
    Li, N., Li, T., Venkatasubramanian, S.: t-Closeness: privacy beyond k-Anonymity and l-Diversity. In: ICDE, pp. 106–115. IEEE Computer Society (2007)Google Scholar
  14. 14.
    Machanavajjhala, A., He, X., Hay, M.: Differential privacy in the wild: a tutorial on current practices & open challenges. PVLDB 9(13), 1611–1614 (2016)Google Scholar
  15. 15.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. TKDD 1(1), 3 (2007)CrossRefGoogle Scholar
  16. 16.
    Oulmakhzoune, S., Cuppens-Boulahia, N., Cuppens, F., Morucci, S.: Privacy policy preferences enforced by SPARQL query rewriting. In: ARES, pp. 335–342. IEEE Computer Society (2012)Google Scholar
  17. 17.
    Radulovic, F., García-Castro, R., Gómez-Pérez, A.: Towards the anonymisation of RDF data. In: SEKE, pp. 646–651. KSI Research Inc. (2015)Google Scholar
  18. 18.
    Sweeney, L.: k-Anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Villata, S., Delaforge, N., Gandon, F., Gyrard, A.: An access control model for linked data. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2011. LNCS, vol. 7046, pp. 454–463. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25126-9_57CrossRefGoogle Scholar
  20. 20.
    W3C: RDF schema 1.1 (2004). http://www.w3.org/TR/rdf-schema/

Copyright information

© Springer Nature Switzerland AG 2018

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

Personalised recommendations