Skip to main content
SpringerLink
Log in
Book cover

International Conference on Information Systems Security and Privacy

ICISSP 2016: Information Systems Security and Privacy pp 108–129Cite as

A Snow-Ball Algorithm for Automating Privacy Policy Configuration in Social Media

  • Conference paper
  • First Online:

  • 603 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 691))

Abstract

Recommender systems offer an effective method of alleviating privacy policy usability issues on Social Media Platforms. However, matching usability with privacy preservation is a challenging problem. In this paper, we first present a comparative analysis to demonstrate that supporting recommender systems with the Random Forest Algorithm (RFA) instead of the J48 decision tree algorithm results in an extra improvement of 5.7% in classification accuracy and that when feature vectors are extended, added classification accuracies of 15%–25% on average are achieved. This improvement comes at the cost of performance, from the usability perspective, due to the high requirement of manual user input. We address this issue with a “Snow-Ball” algorithm that reduces the user input requirement by 81.8%–87.5% and works by employing privacy policy correlation graphs. Our results indicate that the accuracy levels are similar to those of the RFA with manual user input.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    In the recommender systems’ literature, the user that receives the recommendations (i.e. suggestions) is usually referred to as the “target user”.

  2. 2.

    Since the recommender is designed to be a server-side solution, we expect that it will have direct access to users’ profiles’ data.

  3. 3.

    For more information about the simulated datasets, and the simulation model used to generated them, please refer to [1].

  4. 4.

    This can be done by slightly changing any known algorithm for finding minimum spanning trees such as Kruskal’s algorithm.

References

  1. Abuelgasim, A., Kayem, A.: An approach to personalized privacy policy recommendation on online social networks. In: Proceedings of the 2nd International Conference for Information Systems Security and Privacy (ICISSP), pp. 126–137. SCITEPRESS, February 2016

    Google Scholar 

  2. Acquisti, A., Carrara, E., Stutzman, F., Callas, J., Schimmer, K., Nadjm, M., Gorge, M., Ellison, N., King, P., Gross, R., Golder, S.: Security issues and recommendations for online social networks. Technical report 1, European Network and Information Security Agency (2007)

    Google Scholar 

  3. Acock, A.C., Stavig, G.R.: A measure of association for nonparametric statistics. Soc. Forces 57(4), 1381–1386 (1979). http://www.jstor.org/stable/2577276

    Article  Google Scholar 

  4. Alsalibi, B., Zakaria, N.: CFPRS: collaborative filtering privacy recommender system for online social networks. J. Eng. Res. Appl. 3(5), 1850–1858 (2013)

    Google Scholar 

  5. Facebook Inc.: Statistics, September 2015. https://newsroom.fb.com/company-info/

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

    Google Scholar 

  7. Fire, M., Goldschmidt, R., Elovici, Y.: Online social networks: threats and solutions. IEEE Commun. Surv. Tutor. 16(4), 2019–2036 (2014)

    Article  Google Scholar 

  8. Gao, H., Hu, J., Huang, T., Wang, J., Chen, Y.: Security issues in online social networks. IEEE Internet Comput. 15(4), 56–63 (2011)

    Article  Google Scholar 

  9. Ghazinour, K., Matwin, S., Sokolova, M.: Monitoring and recommending privacy settings in social networks. In: Proceedings of the Joint EDBT/ICDT 2013 Workshops, EDBT 2013, pp. 164–168. ACM, New York (2013)

    Google Scholar 

  10. Ghazinour, K., Matwin, S., Sokolova, M.: YourPrivacyProtector: a recommender system for privacy settings in social networks. Int. J. Secur. 2(4), 11–25 (2013)

    Google Scholar 

  11. Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. In: Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society, WPES 2005, pp. 71–80. ACM, New York (2005)

    Google Scholar 

  12. Gross, R., Stutzman, F., Acquisti, A.: Silent listeners: the evolution of privacy and disclosure on Facebook. J. Priv. Confid. 4(2), 7–41 (2013)

    Google Scholar 

  13. Gundecha, P., Barbier, G., Liu, H.: Exploiting vulnerability to secure user privacy on a social networking site. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 511–519, USA (2011). http://doi.acm.org/10.1145/2020408.2020489

  14. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The Weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009). http://doi.acm.org/10.1145/1656274.1656278, http://www.cs.waikato.ac.nz/ml/weka/

  15. Liu, Y., Gummadi, K.P., Krishnamurthy, B., Mislove, A.: Analyzing Facebook privacy settings: user expectations vs. reality. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference. IMC 2011, pp. 61–70. ACM, New York (2011)

    Google Scholar 

  16. Madejski, M., Johnson, M., Bellovin, S.: A study of privacy settings errors in an online social network. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 340–345. IEEE, March 2012

    Google Scholar 

  17. Mitchell, T.M.: Decision tree learning. In: Tucker, C.L.B. (ed.) Machine Learning. Chapter 3, 1st edn, pp. 52–80. McGraw-Hill Inc., New York (1997)

    Google Scholar 

  18. Core Team, R.: R: a language and environment for statistical computing. R foundation for statistical computing, Vienna (2016). https://www.R-project.org/

  19. Sánchez, D., Viejo, A.: Privacy risk assessment of textual publications in social networks. In: Proceedings of the International Conference on Agents and Artificial Intelligence, pp. 236–241 (2015)

    Google Scholar 

  20. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)

    Article  MathSciNet  Google Scholar 

  21. Shehab, M., Cheek, G., Touati, H., Squicciarini, A.C., Cheng, P.C.: Learning based access control in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 1179–1180. ACM, New York (2010)

    Google Scholar 

  22. Sinha, A., Li, Y., Bauer, L.: What you want is not what you get: predicting sharing policies for text-based content on Facebook. In: Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security - AISec 2013, pp. 13–24. ACM, New York (2013)

    Google Scholar 

  23. Toch, E., Sadeh, N.M., Hong, J.: Generating default privacy policies for online social networks. In: CHI 2010 Extended Abstracts on Human Factors in Computing Systems, CHI EA 2010, pp. 4243–4248. ACM, New York (2010)

    Google Scholar 

  24. Twitter: Statistics, September 2015. https://about.twitter.com/company

Download references

Acknowledgements

The authors gratefully acknowledge funding for this research provided by the National Research Foundation (NRF) of South Africa, and the Norwegian National Research Council.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cape Town, Rondebosch 7701, Cape Town, South Africa

    Ammar Abuelgasim

  2. Internet Technologies and Systems, Hasso-Plattner Institute, Prof.-Dr.-Helmert-Str. 2-3, Potsdam, 14482, Germany

    Anne V. D. M. Kayem

Authors
  1. Ammar Abuelgasim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anne V. D. M. Kayem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ammar Abuelgasim .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Olivier Camp

  2. Plymouth University, Plymouth, United Kingdom

    Steven Furnell

  3. Consiglio Nazionale delle Ricerche, Pisa, Italy

    Paolo Mori

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Abuelgasim, A., Kayem, A.V.D.M. (2017). A Snow-Ball Algorithm for Automating Privacy Policy Configuration in Social Media. In: Camp, O., Furnell, S., Mori, P. (eds) Information Systems Security and Privacy. ICISSP 2016. Communications in Computer and Information Science, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-319-54433-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54433-5_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54432-8

  • Online ISBN: 978-3-319-54433-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation