Skip to main content
SpringerLink
Log in
Book cover

European Network Intelligence Conference

ENIC 2017: Network Intelligence Meets User Centered Social Media Networks pp 187–197Cite as

Towards an ILP Approach for Learning Privacy Heuristics from Users’ Regrets

  • Chapter
  • First Online:

  • 358 Accesses

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Disclosing private information in Social Network Sites (SNSs) often results in unwanted incidents for the users (such as bad image, identity theft, or unjustified discrimination), along with a feeling of regret and repentance. Regrettable online self-disclosure experiences can be seen as sources of privacy heuristics (best practices) that can help shaping better privacy awareness mechanisms. Considering deleted posts as an explicit manifestation of users’ regrets, we propose an Inductive Logic Programming (ILP) approach for learning privacy heuristics. In this paper we introduce the motivating scenario and the theoretical foundations of this approach, and we provide an initial assessment towards its implementation.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Let e(X) be the predicate which defines the examples, and L = L 1, …, L n a set of ground literals which subsume the variable X. Then, positive examples can be expressed as e(L i)., and negative examples as : −e(L j), ∀ 1 ≤ i, j ≤ n.

  2. 2.

    A clause c 1 θ-subsumes a clause c 2 if and only if there exists a substitution θ such that c 1 θ ⊆ c 2. Consequently c 1 is a generalization of c 2 (and c 2 specialization of c 1) under θ-subsumption [8].

  3. 3.

    SAs are those which can be linked to an individual, groups or communities and can raise privacy concerns related to data aggregation, probabilistic re-identification and undesirable social categorizations [5].

References

  1. Athakravi, D., Broda, K., Russo, A.: Predicate invention in inductive logic programming. In: OASIcs-OpenAccess Series in Informatics, vol. 28. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2012)

    Google Scholar 

  2. Calikli, G., Law, M., Bandara, A.K., Russo, A., Dickens, L., Price, B.A., Stuart, A., Levine, M., Nuseibeh, B.: Privacy dynamics: learning privacy norms for social software. In: Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-managing Systems, May 2016, pp. 47–56. ACM, New York (2016)

    Google Scholar 

  3. Diaz, C., Gürses, S.: Understanding the landscape of privacy technologies (extended abstract). In: Proceedings of the Information Security Summit, ISS, May 2012, pp. 58–63 (2012)

    Google Scholar 

  4. Díaz Ferreyra, N.E., Schäwel, J., Heisel, M., Meske, C.: Addressing self-disclosure in social media: an instructional awareness approach. In: Proceedings of the 2nd ACS/IEEE International Workshop on Online Social Networks Technologies (OSNT), December 2016. ACS/IEEE, Washington/Piscataway (2016)

    Google Scholar 

  5. Diaz Ferreyra, N. E., Meis, R., Heisel, M.: Online self-disclosure: from users’ regrets to instructional awareness. In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer, 2017, pp. 83–102 Díaz Ferreyra, N.E., Meis, R., Heisel, M.: Online self-disclosure: from users’ regrets to instructional awareness. In: Proceedings of the IFIP International Cross-Domain Conference (CD-MAKE) (August 2017) (accepted for publication)

    Google Scholar 

  6. Fang, L., LeFevre, K.: Privacy wizards for social networking sites. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 351–360. ACM, New York (2010). http://doi.acm.org/10.1145/1772690.1772727

  7. Muggleton, S.: Inverse entailment and progol. New Gener. Comput. 13(3), 245–286 (1995)

    Article  Google Scholar 

  8. Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. Log. Program. 19, 629–679 (1994)

    Article  Google Scholar 

  9. Muggleton, S.H., Firth, J.: CProgol4.4: a tutorial introduction. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining, pp. 160–188, Springer, Berlin (2001). http://www.doc.ic.ac.uk/~shm/Papers/progtuttheo.pdf

    Google Scholar 

  10. Nissenbaum, H.: Privacy as contextual integrity. Wash. L. Rev. 79, 119 (2004)

    Google Scholar 

  11. Roberts, S.: An Introduction to Progol. Department of Computer Science, University of York, Heslington, York (1997)

    Google Scholar 

  12. Westin, A.F.: Privacy and freedom. Wash. Lee L. Rev. 25(1), 166 (1968)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under grant No. GRK 2167, Research Training Group “User-Centred Social Media”.

Author information

Authors and Affiliations

  1. University of Duisburg Essen, Duisburg, Germany

    Nicolás Emilio Díaz Ferreyra, Rene Meis & Maritta Heisel

Authors
  1. Nicolás Emilio Díaz Ferreyra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rene Meis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maritta Heisel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolás Emilio Díaz Ferreyra .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Nordrhein-Westfalen, Germany

    H. Ulrich Hoppe

  3. Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Nordrhein-Westfalen, Germany

    Tobias Hecking

  4. Department of Computational Intelligence The Engine Centre, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Piotr Bródka

  5. Faculty of Computer Science and Management The Engine Centre, Wroclaw University of Science and Technology, Wroclaw, Poland

    Przemyslaw Kazienko

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ferreyra, N.E.D., Meis, R., Heisel, M. (2018). Towards an ILP Approach for Learning Privacy Heuristics from Users’ Regrets. In: Alhajj, R., Hoppe, H., Hecking, T., Bródka, P., Kazienko, P. (eds) Network Intelligence Meets User Centered Social Media Networks. ENIC 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-90312-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90312-5_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90311-8

  • Online ISBN: 978-3-319-90312-5

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics

Search

Navigation