Advertisement

PriGuardTool: A Web-Based Tool to Detect Privacy Violations Semantically

  • Nadin Kökciyan
  • Pınar Yolum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10093)

Abstract

Online social networks contain plethora of information about its users. While users enjoy sharing information online, not all information is meant to be seen by the entire network. Managing the privacy of users has become an important aspect of such online networks. An important part of this is detecting privacy violations and notifying the users so that they can take appropriate actions. While various approaches for detecting privacy violations exist, most of the approaches do not have a running tool that can exhibit the principles of its underlying approach.

This paper presents PriGuardTool, a Web-based tool that can detect privacy violations in online social networks. Each user is represented by a software agent in the system that first collects user’s privacy concerns, explicitly specified as what types of content are meant to be seen by which audience. The system represents these privacy constraints as commitments between the user and the online social network. The user constraints are converted into commitments automatically by the agent. The system then monitors which commitments are violated based on the content shown to users, such that a violated commitment represents a privacy violation in the system. While checking for violations, the effects of posts on the system as well as the semantic relations and rules are considered. We evaluate PriGuardTool by using various real-life scenarios and real data that have been collected over Facebook. Our initial results show that realistic privacy violations can be detected using PriGuardTool.

Keywords

Privacy Online social networks Commitment Ontology 

Notes

Acknowledgments

This work is supported by TUBITAK under grant 113E543. This work extends the demonstration paper that was presented at AAMAS 2016 [13]. We thank Hamza Ozturk and Safa Orhan for helping with integrating PriGuardTool to Facebook.

References

  1. 1.
    Akcora, C.G., Carminati, B., Ferrari, E.: Risks of friendships on social networks. In: IEEE International Conference on Data Mining (ICDM), pp. 810–815 (2012)Google Scholar
  2. 2.
    Baldoni, M., Baroglio, C., Chopra, A.K., Singh, M.P.: Composing and verifying commitment-based multiagent protocols. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 10–17 (2015)Google Scholar
  3. 3.
    Carminati, B., Ferrari, E., Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Semantic web-based social network access control. Comput. Secur. 30(2), 108–115 (2011)CrossRefGoogle Scholar
  4. 4.
    Fang, L., LeFevre, K.: Privacy wizards for social networking sites. In: Proceedings of the 19th International Conference on World Wide Web, pp. 351–360. ACM (2010)Google Scholar
  5. 5.
    Gürses, F., Berendt, B.: The social web and privacy: practices, reciprocity and conflict detection in social networks. In: Privacy-Aware Knowledge Discovery, pp. 395–432. Chapman & Hall/CRC Press, New York (2010)Google Scholar
  6. 6.
    Heussner, K.M.: Celebrities’ photos, videos may reveal location. ABC News. http://goo.gl/sJIFg4
  7. 7.
    Hu, H., Ahn, G.J., Jorgensen, J.: Multiparty access control for online social networks: model and mechanisms. IEEE Trans. Knowl. Data Eng. 25(7), 1614–1627 (2013)CrossRefGoogle Scholar
  8. 8.
    Kafalı, O., Günay, A., Yolum, P.: Detecting and predicting privacy violations in online social networks. Distrib. Parallel Databases 32(1), 161–190 (2014)CrossRefGoogle Scholar
  9. 9.
    Keküllüoğlu, D., Kökciyan, N., Yolum, P.: Strategies for privacy negotiation in online social networks. In: Proceedings of the 1st International Workshop on AI for Privacy and Security (PrAISe), pp. 2:1–2:8 (2016)Google Scholar
  10. 10.
    Kepez, B., Yolum, P.: Learning privacy rules cooperatively in online social networks. In: Proceedings of the 1st International Workshop on AI for Privacy and Security (PrAISe), pp. 3:1–3:4. ACM (2016)Google Scholar
  11. 11.
    Kökciyan, N., Yaglikci, N., Yolum, P.: Argumentation for resolving privacy disputes in online social networks: (extended abstract). In: Proceedings of the 15th International Conference on Autonomous Agents & Multiagent Systems, Singapore, May 9–13, pp. 1361–1362 (2016)Google Scholar
  12. 12.
    Kökciyan, N., Yolum, P.: Priguard: a semantic approach to detect privacy violations in online social networks. IEEE Trans. Knowl. Data Eng. 28(10), 2724–2737 (2016)CrossRefGoogle Scholar
  13. 13.
    Kökciyan, N., Yolum, P.: PriGuardTool: a tool for monitoring privacy violations in online social networks (demonstration). In: Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1496–1497 (2016)Google Scholar
  14. 14.
    Krishnamurthy, B.: Privacy and online social networks: can colorless green ideas sleep furiously? IEEE Secur. Priv. 11(3), 14–20 (2013)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lampinen, A., Lehtinen, V., Lehmuskallio, A., Tamminen, S.: We’re in it together: interpersonal management of disclosure in social network services. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3217–3226. ACM (2011)Google Scholar
  16. 16.
    Liu, K., Terzi, E.: A framework for computing the privacy scores of users in online social networks. ACM Trans. Knowl. Discov. Data (TKDD) 5(1), 6: 1–6: 30 (2010)Google Scholar
  17. 17.
    Mester, Y., Kökciyan, N., Yolum, P.: Negotiating privacy constraints in online social networks. In: Koch, F., Guttmann, C., Busquets, D. (eds.) Advances in Social Computing and Multiagent Systems, Communications in Computer and Information Science, vol. 541, pp. 112–129. Springer, Heidelberg (2015)Google Scholar
  18. 18.
    Motik, B., Patel-Schneider, P.F., Parsia, B., Bock, C., Fokoue, A., Haase, P., Hoekstra, R., Horrocks, I., Ruttenberg, A., Sattler, U., et al.: Owl 2 web ontology language: structural specification and functional-style syntax. W3C Recommendation 27(65), 159 (2009)Google Scholar
  19. 19.
    Mugan, J., Sharma, T., Sadeh, N.: Understandable learning of privacy preferences through default personas and suggestions. Technical report CMU-ISR-11-112, School of Computer Science, Carnegie Mellon University (2011)Google Scholar
  20. 20.
    Pappachan, P., Yus, R., Das, P.K., Finin, T., Mena, E., Joshi, A.: A semantic context-aware privacy model for faceblock. In: Proceedings of the 2nd International Conference on Society, Privacy and the Semantic Web - Policy and Technology, pp. 64–72. PrivOn (2014)Google Scholar
  21. 21.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 16 (2009)CrossRefGoogle Scholar
  22. 22.
    Singh, M.P.: An ontology for commitments in multiagent systems. Artif. Intell. Law 7(1), 97–113 (1999)CrossRefGoogle Scholar
  23. 23.
    Squicciarini, A.C., Paci, F., Sundareswaran, S.: PriMa: a comprehensive approach to privacy protection in social network sites. Ann. Telecommun./Annales des Télécommunications 69(1), 21–36 (2014)Google Scholar
  24. 24.
    Squicciarini, A.C., Xu, H., Zhang, X.L.: Cope: enabling collaborative privacy management in online social networks. J. Am. Soc. Inf. Sci. Technol. 62(3), 521–534 (2011)Google Scholar
  25. 25.
    Such, J.M., Rovatsos, M.: Privacy policy negotiation in social media. ACM Trans. Auton. Adapt. Syst. (TAAS) 11(1), 4:1–4:29 (2016)Google Scholar
  26. 26.
    Yolum, P., Singh, M.P.: Flexible protocol specification and execution: applying event calculus planning using commitments. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 527–534 (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer EngineeringBogazici UniversityBebek, IstanbulTurkey

Personalised recommendations