Skip to main content

Implementing Informed Consent with Knowledge Graphs

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12739)

Abstract

The GDPR legislation has brought to light one’s rights and has highlighted the importance of consent, which has caused a major shift in how data processing and sharing are handled. Data sharing has been a popular research topic for many years, however, a unified solution for the transparent implementation of consent, in compliance with GDPR that could be used as a standard, has not been presented yet. This research proposes a solution for implementing informed consent for sensor data sharing in compliance with GDPR with semantic technology, namely knowledge graphs. The main objectives are to model the life cycle of informed consent (i.e. the request, comprehension, decision and use of consent) with knowledge graphs so that it is easily interpretable by machines, and to graphically visualise it to individuals in order to raise legal awareness of what it means to consent and the implications that follow.

Keywords

  • Knowledge graph
  • Knowledge graph visualisation
  • GDPR
  • Informed consent
  • Sensor data
  • Data sharing
  • Legal comprehension

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-80418-3_28
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-80418-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Notes

  1. 1.

    https://eur-lex.europa.eu/eli/reg/2016/679/oj.

  2. 2.

    https://openscience.adaptcentre.ie/ontologies/consent/docs/index-en.html.

  3. 3.

    http://openscience.adaptcentre.ie/ontologies/GConsent/doc.

  4. 4.

    https://www.bpr4gdpr.eu/wp-content/uploads/2019/06/D3.1-Compliance-Ontology-1.0.pdf.

  5. 5.

    https://dpvcg.github.io/dpv/.

  6. 6.

    https://ai.wu.ac.at/policies/policylog/.

  7. 7.

    https://www.dalicc.net.

  8. 8.

    https://www.w3.org/TR/odrl-model/.

  9. 9.

    https://www.hpl.hp.com/breweb/encoreproject/index.html.

  10. 10.

    https://projekte.ffg.at/projekt/3314668.

  11. 11.

    https://www.smashhit.eu.

  12. 12.

    https://kantarainitiative.org/file-downloads/consent-receipt-specification-v1-1-0/.

  13. 13.

    https://www.iso.org/standard/70331.html.

  14. 14.

    https://iabeurope.eu/transparency-consent-framework/.

  15. 15.

    https://www.smashhit.eu.

  16. 16.

    https://github.com/aneliamk/consent/raw/a8da0b3ae8f48f282f1006a68adea55c4857bdc9/ontology/Consent_Sensor.owl.

  17. 17.

    https://protege.stanford.edu.

  18. 18.

    https://spec.edmcouncil.org/fibo/.

  19. 19.

    https://www.w3.org/TR/vocab-ssn/.

  20. 20.

    https://graphdb.ontotext.com.

  21. 21.

    https://github.com/aneliamk/CampaNeoUI.

  22. 22.

    https://flutter.dev.

  23. 23.

    https://d3js.org.

  24. 24.

    https://www.w3.org/TR/rdf-sparql-query/.

  25. 25.

    http://ontoware.org/projects/ontomanager.

  26. 26.

    https://www.w3.org/TR/shacl/.

  27. 27.

    https://www.nngroup.com/articles/thinking-aloud-the-1-usability-tool/.

  28. 28.

    https://gdpr.eu/data-protection-impact-assessment-template/.

  29. 29.

    https://www.datenschutzzentrum.de/uploads/sdm/SDM-Methodology_V2.0b.pdf.

References

  1. Angulo, J., Fischer-Hübner, S., Pulls, T., Wästlund, E.: Usable transparency with the data track: a tool for visualizing data disclosures. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (2015)

    Google Scholar 

  2. Bechmann, A.: Non-informed consent cultures: privacy policies and app contracts on Facebook. J. Media Bus. Stud. 11(1), 21–38 (2014). https://doi.org/10.1080/16522354.2014.11073574

    CrossRef  Google Scholar 

  3. Bikakis, N., Skourla, M., Papastefanatos, G.: rdf:SynopsViz - a framework for hierarchical linked data visual exploration and analysis. ArXiv abs/1408.3148 (2014)

    Google Scholar 

  4. Bretzke, H., Vassileva, J.: Motivating cooperation on peer to peer networks. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds.) UM 2003. LNCS (LNAI), vol. 2702, pp. 218–227. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-44963-9_30

    CrossRef  Google Scholar 

  5. Brunetti, J.M., Auer, S., García, R.: The linked data visualization model. In: Proceedings of the 2012th International Conference on Posters and Demonstrations Track, vol. 914. pp. 5–8. CEUR-WS.org (2012)

    Google Scholar 

  6. Cagáňová, D., Stareček, A., Horňáková, N., Hlásniková, P.: The analysis of the Slovak citizens’ awareness about the smart city concept. Mob. Netw. Appl. 24(6), 2050–2058 (2019). https://doi.org/10.1007/s11036-018-01210-6

    CrossRef  Google Scholar 

  7. Cardoso, J., Hepp, M., Lytras, M.: The Semantic Web: Real-World Applications from Industry. Springer, Heidelberg (2008). https://doi.org/10.1007/978-0-387-48531-7

    CrossRef  Google Scholar 

  8. Corcho, Ó., Gómez-Pérez, A., González-Cabero, R., Suárez-Figueroa, M.C.: ODEval: a tool for evaluating RDF(S), DAML+OIL, and OWL concept taxonomies. In: Bramer, M., Devedzic, V. (eds.) AIAI 2004. IIFIP, vol. 154, pp. 369–382. Springer, Boston, MA (2004). https://doi.org/10.1007/1-4020-8151-0_32

    CrossRef  Google Scholar 

  9. Drozd, O., Kirrane, S.: I agree: customize your personal data processing with the CoRe user interface. In: Gritzalis, S., Weippl, E.R., Katsikas, S.K., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) TrustBus 2019. LNCS, vol. 11711, pp. 17–32. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27813-7_2

    CrossRef  Google Scholar 

  10. Drozd, O., Kirrane, S.: Privacy CURE: consent comprehension made easy. In: Hölbl, M., Rannenberg, K., Welzer, T. (eds.) SEC 2020. IAICT, vol. 580, pp. 124–139. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58201-2_9

    CrossRef  Google Scholar 

  11. Fatema, K., Hadziselimovic, E., Pandit, H.J., Debruyne, C., Lewis, D., O’Sullivan, D.: Compliance through informed consent: semantic based consent permission and data management model. In: PrivOn@ ISWC (2017)

    Google Scholar 

  12. Fensel, A., Tomic, S., Kumar, V., Stefanovic, M., Aleshin, S., Novikov, D.: SESAME-S: semantic smart home system for energy efficiency. Informatik-Spektrum 36, 46–57 (2012). https://doi.org/10.1007/s00287-012-0665-9

    CrossRef  Google Scholar 

  13. Fensel, D., et al.: Knowledge Graphs: Methodology, Tools and Selected Use Cases. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37439-6

    CrossRef  Google Scholar 

  14. Gross, B.M.: The managing of organizations: the administrative struggle. Ann. Am. Acad. Polit. Soc. Sci. 360(1), 197–198 (1965). https://doi.org/10.1177/000271626536000140

    CrossRef  Google Scholar 

  15. Hogan, A., et al.: Knowledge graphs. Commun. ACM 64, 96–104 (2020)

    Google Scholar 

  16. Jaiman, V., Urovi, V.: A consent model for blockchain-based health data sharing platforms. IEEE Access 8, 143734–143745 (2020). https://doi.org/10.1109/ACCESS.2020.3014565

    CrossRef  Google Scholar 

  17. Kirrane, S., Fernández, J.D., Bonatti, P., Milosevic, U., Polleres, A., Wenning, R.: The special-k personal data processing transparency and compliance platform. ArXiv abs/2001.09461 (2020)

    Google Scholar 

  18. Ma, X., et al.: Data visualization in the semantic web. In: Semantic Web Enabled Software Engineering (2015)

    Google Scholar 

  19. Mahindrakar, A., Joshi, K.P., et al.: Automating GDPR compliance using policy integrated blockchain. In: IEEE 6th International Conference on Big Data Security on Cloud (BigDataSecurity 2020) (2020)

    Google Scholar 

  20. Mallardi, V.: The origin of informed consent. Acta otorhinolaryngologica Italica: organo ufficiale della Società italiana di otorinolaringologia e chirurgia cervico-facciale 25, 312–27 (2005)

    Google Scholar 

  21. Meisel, A., Kabnick, L.D.: Informed consent to medical treatment: an analysis of recent legislation. Univ. Pitt. Law Rev. 41(3), 407–564 (1980). University of Pittsburgh. School of Law

    Google Scholar 

  22. Noy, N.: Ontology development 101: a guide to creating your first ontology. Technical report KSL-01-05 and SMI-2001-0880, Stanford Knowledge Systems Laboratory and Stanford Medical Informatics (2001)

    Google Scholar 

  23. Pandit, H.J., Debruyne, C., O’Sullivan, D., Lewis, D.: GConsent - a consent ontology based on the GDPR. In: Hitzler, P., et al. (eds.) ESWC 2019. LNCS, vol. 11503, pp. 270–282. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21348-0_18

    CrossRef  Google Scholar 

  24. Pellegrini, T., Mireles, V., Steyskal, S., Panasiuk, O., Fensel, A., Kirrane, S.: Automated rights clearance using semantic web technologies: the DALICC framework. In: Hoppe, T., Humm, B., Reibold, A. (eds.) Semantic Applications, pp. 203–218. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-55433-3_14

    CrossRef  Google Scholar 

  25. Rantos, K., Drosatos, G., Demertzis, K., Ilioudis, C., Papanikolaou, A., Kritsas, A.: ADvoCATE: a consent management platform for personal data processing in the IoT using blockchain technology. In: Lanet, J.-L., Toma, C. (eds.) SECITC 2018. LNCS, vol. 11359, pp. 300–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12942-2_23

    CrossRef  Google Scholar 

  26. Raschke, P., Küpper, A., Drozd, O., Kirrane, S.: Designing a GDPR-compliant and usable privacy dashboard. In: Hansen, M., Kosta, E., Nai-Fovino, I., Fischer-Hübner, S. (eds.) Privacy and Identity 2017. IAICT, vol. 526, pp. 221–236. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92925-5_14

    CrossRef  Google Scholar 

  27. Rodrigues, L., Toda, A., Oliveira, W., Palomino, P., Vassileva, J., Isotani, S.: Automating gamification personalization: to the user and beyond. ArXiv abs/2101.05718 (2021)

    Google Scholar 

  28. Sermet, Y., Demir, I.: A semantic web framework for automated smart assistants: Covid-19 case study. ArXiv abs/2007.00747 (2020)

    Google Scholar 

  29. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of IEEE Symposium on Visual Languages, pp. 336–343 (1996)

    Google Scholar 

  30. Simsek, U., et al.: A semantic approach towards implementing energy efficient lifestyles through behavioural change. In: Proceedings of the 12th International Conference on Semantic Systems, pp. 173–176 (2016)

    Google Scholar 

  31. Stuhr, M., Roman, D., Norheim, D.: LODWheel - Javascript-based visualization of RDF data. In: Proceedings of the 2nd International Workshop on Consuming Linked Data (COLD 2011). CEUR Workshop Proceedings, vol. 782, pp. 73–84 (2011)

    Google Scholar 

  32. Wolf, S., Clayton, E., Lawrenz, F.: The past, present, and future of informed consent in research and translational medicine. J. Law Med. Ethics 46, 7–11 (2018)

    CrossRef  Google Scholar 

Download references

Acknowledgements

This research is supported by the smashHit project funded under Horizon 2020 (grant 871477) and CampaNeo project funded by FFG (grant 873839). The main supervisor of this thesis is Assoc. Prof. Dr. Anna Fensel. I would like to thank Antonio Roa Valverde, Christof Bless, Lukas Dötlinger, Manuel Penz, Markus Reiter, Michael Kaltschmid, Petraq Nako, Stephanie Widauer, Sven Rasmusen for their support in the CampaNeo project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anelia Kurteva .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Kurteva, A. (2021). Implementing Informed Consent with Knowledge Graphs. In: , et al. The Semantic Web: ESWC 2021 Satellite Events. ESWC 2021. Lecture Notes in Computer Science(), vol 12739. Springer, Cham. https://doi.org/10.1007/978-3-030-80418-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-80418-3_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80417-6

  • Online ISBN: 978-3-030-80418-3

  • eBook Packages: Computer ScienceComputer Science (R0)