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Detecting and predicting privacy violations in online social networks

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Abstract

Online social networks have become an essential part of social and work life. They enable users to share, discuss, and create content together with various others. Obviously, not all content is meant to be seen by all. It is extremely important to ensure that content is only shown to those that are approved by the content’s owner so that the owner’s privacy is preserved. Generally, online social networks are promising to preserve privacy through privacy agreements, but still everyday new privacy leakages are taking place. Ideally, online social networks should be able to manage and maintain their agreements through well-founded methods. However, the dynamic nature of the online social networks is making it difficult to keep private information contained.

We have developed \(\mathcal{PROTOSS}\), a run time tool for detecting and predicting \(\mathcal{PR}\mathrm{ivacy}\ \mathrm{vi}\mathcal{O}\mathrm{la}\mathcal{T}\mathrm{ions}\ \mathrm{in}\ \mathcal{O}\mathrm{nline}\ \mathcal{S}\mathrm{ocial}\ \mathrm{network}\mathcal{S}\). \(\mathcal{PROTOSS}\) captures relations among users, their privacy agreements with an online social network operator, as well as domain-based semantic information and rules. It uses model checking to detect if relations among the users will result in the violation of privacy agreements. It can further use the semantic information to infer possible violations that have not been specified by the user explicitly. In addition to detection, \(\mathcal{PROTOSS}\) can predict possible future violations by feeding in a hypothetical future world state. Through a running example, we show that \(\mathcal{PROTOSS}\) can detect and predict subtle leakages, similar to the ones reported in real life examples. We study the performance of our system on the scenario as well as on an existing Facebook dataset.

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Notes

  1. This implementation can be downloaded from http://mas.cmpe.boun.edu.tr/ozgur/code.html, under Sect. “5. Experiments for Predicting Privacy Violations with PROTOSS”.

  2. This implementation can be downloaded from http://mas.cmpe.boun.edu.tr/ozgur/code.html, under Sect. “4. Experiments for Model Checking Privacy Agreements”.

  3. In order to have a performance gain, we stop expanding the network at the friends of friends level. Recent work on link prediction in social networks [3] have shown that it is very likely for a new friend to be already contained in this friends of friends network.

References

  1. Lane v. Facebook, Inc.: Wikipedia entry. Available at: http://en.wikipedia.org/wiki/Lane_v._Facebook,_Inc

  2. Akcora, C.G., Carminati, B., Ferrari, E.: Privacy in social networks: How risky is your social graph? In: Proceedings of the 28th International Conference on Data Engineering (ICDE), pp. 9–19 (2012)

    Google Scholar 

  3. Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pp. 635–644. ACM, New York (2011)

    Chapter  Google Scholar 

  4. Baden, R., Bender, A., Spring, N., Bhattacharjee, B., Starin, D., Persona: an online social network with user-defined privacy. In: Proceedings of the ACM SIGCOMM 2009 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM), pp. 135–146 (2009)

    Chapter  Google Scholar 

  5. Bentahar, J., El-Menshawy, M., Dssouli, R.: An integrated semantics of social commitments and associated operations. In: Proceedings of the Second Multi-agent Logics, Languages, and Organisations Federated Workshops (2009). The paper can be accessed for free via the link http://ceur-ws.org/Vol-494/ladspaper3.pdf

    Google Scholar 

  6. Carminati, B., Ferrari, E.: Privacy-aware access control in social networks: issues and solutions. In: Privacy and Anonymity in Information Management Systems, pp. 181–195. Springer, Berlin (2010), Chap. 9

    Chapter  Google Scholar 

  7. Cimatti, A., Clarke, E., Giunchiglia, E., Giunchiglia, F., Pistore, M., Roveri, M., Sebastiani, R., Tacchella, A.: NuSMV version 2: An OpenSource tool for symbolic model checking. In: Proc. International Conference on Computer-Aided Verification (CAV 2002). LNCS, vol. 2404. Springer, Copenhagen (2002)

    Google Scholar 

  8. Clarke, E.M., Emerson, E.A.: Design and synthesis of synchronization skeletons using branching-time temporal logic. In: Logic of Programs, Workshop, pp. 52–71. Springer, London (1982)

    Chapter  Google Scholar 

  9. Debatin, B., Lovejoy, J.P., Horn, A.K., Hughes, B.N.: Facebook and online privacy: attitudes, behaviors, and unintended consequences. J. Comput.-Mediat. Commun. 15(1), 83–108 (2009)

    Article  Google Scholar 

  10. El Menshawy, M., Bentahar, J., Qu, H., Dssouli, R.: On the verification of social commitments and time. In: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 483–490 (2011)

    Google Scholar 

  11. Emerson, E.A.: Temporal and modal logic. In: Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics (B), pp. 995–1072. MIT Press, Cambridge (1990)

    Google Scholar 

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

    Chapter  Google Scholar 

  13. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)

    Article  Google Scholar 

  14. Heussner, K.M.: Celebrities’ photos, videos may reveal location. ABC News. Available at: http://abcnews.go.com/Technology/celebrity-stalking-online-photos-give-location/story?id=11162352

  15. Huth, M., Ryan, M.: Logic in Computer Science: Modelling and Reasoning About Systems, 2nd edn. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  16. Krishnamurthy, B., Wills, C.E.: On the leakage of personally identifiable information via online social networks. Comput. Commun. Rev. 40(1), 112–117 (2010)

    Article  Google Scholar 

  17. Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 462–470. ACM, New York (2008)

    Chapter  Google Scholar 

  18. Li, N., Zhang, N., Das, S.K.: Preserving relation privacy in online social network data. IEEE Internet Comput. 15(3), 35–42 (2011)

    Article  Google Scholar 

  19. McGuinness, D.L.: Ontologies come of age. In: Spinning the Semantic Web, pp. 171–194. MIT Press, Cambridge (2003)

    Google Scholar 

  20. Resnik, P.: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999)

    MATH  Google Scholar 

  21. Singh, M.P.: An ontology for commitments in multiagent systems: toward a unification of normative concepts. Artif. Intell. Law 7, 97–113 (1999)

    Article  Google Scholar 

  22. Telang, P., Singh, M.: Specifying and verifying cross-organizational business models: an agent-oriented approach. IEEE Trans. Serv. Comput. 4 (2011)

  23. Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Social Networks (WOSN’09) (2009)

    Google Scholar 

  24. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics (ACL), pp. 133–138 (1994)

    Chapter  Google Scholar 

  25. Xue, M., Carminati, B., Ferrari, E.: P3d—privacy-preserving path discovery in decentralized online social networks. In: Proceedings of the 35th Annual IEEE International Computer Software and Applications Conference (COMPSAC), pp. 48–57. IEEE Computer Society, New York (2011)

    Google Scholar 

  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 (AAMAS), pp. 527–534 (2002)

    Chapter  Google Scholar 

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Acknowledgements

We are indebted to Alan Mislove for sharing the Facebook dataset. This research is supported by Bogazici University Research Fund under grant BAP5694, and the Turkish State Planning Organization (DPT) under the TAM Project, number 2007K120610. Akın Günay is partially supported by a TÜBİTAK Scholarship (2211). Pınar Yolum is partially supported by a TÜBİTAK Scholarship (2219). Most of this work was done while Özgür Kafalı was at Bogazici University, and Pınar Yolum was on sabbatical at Cornell University.

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Correspondence to Pınar Yolum.

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Communicated by Elena Ferrari.

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Kafalı, Ö., Günay, A. & Yolum, P. Detecting and predicting privacy violations in online social networks. Distrib Parallel Databases 32, 161–190 (2014). https://doi.org/10.1007/s10619-013-7124-8

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