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Profile Matching Across Online Social Networks

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Information and Communications Security (ICICS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12282))

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Abstract

In this work, we study the privacy risk due to profile matching across online social networks (OSNs), in which anonymous profiles of OSN users are matched to their real identities using auxiliary information about them. We consider different attributes that are publicly shared by users. Such attributes include both strong identifiers such as user name and weak identifiers such as interest or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to profile matching in order to show the privacy threat in an extensive way. The proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on three datasets (Twitter - Foursquare, Google+ - Twitter, and Flickr) and show how profiles of the users in different OSNs can be matched with high probability by using the publicly shared attributes and/or the underlying graphical structure of the OSNs. We also show that the proposed framework notably provides higher precision values compared to state-of-the-art that relies on machine learning techniques. We believe that this work will be a valuable step to build a tool for the OSN users to understand their privacy risks due to their public sharings.

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Notes

  1. 1.

    Such profiles are required to construct the ground-truth for training.

  2. 2.

    Sets \(\mathrm {A_e}\) and \(\mathrm {T_e}\) do not include any users from sets \(\mathrm {A_t}\) and \(\mathrm {T_t}\).

  3. 3.

    US social security name database includes year of birth, gender, and the corresponding name for babies born in the United States.

  4. 4.

    The case when the sizes of the OSNs are different can be also handled similarly (by padding one OSN with dummy users to equalize the sizes).

References

  1. Google maps API (2020). https://developers.google.com/maps/

  2. Natural language toolkit (2020). http://www.nltk.org/

  3. Swarm (2020). https://www.swarmapp.com/

  4. Twitter streaming API (2020). https://dev.twitter.com/streaming/overview

  5. Amos, B., Ludwiczuk, B., Satyanarayanan, M.: Openface: a general-purpose face recognition library with mobile applications. Technical report, CMU-CS-16-118, CMU School of Computer Science (2016)

    Google Scholar 

  6. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  7. Boyd, D.M., Ellison, N.B.: Social network sites: definition, history, and scholarship. J. Comput.-Mediated Commun. 13(1), 210–230 (2007)

    Article  Google Scholar 

  8. Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis. In: WWW (2008)

    Google Scholar 

  9. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: ACL (2005)

    Google Scholar 

  10. Goga, O., Loiseau, P., Sommer, R., Teixeira, R., Gummadi, K.P.: On the reliability of profile matching across large online social networks. In: KDD (2015)

    Google Scholar 

  11. Halimi, A., Ayday, E.: Profile matching across unstructured online social networks: Threats and countermeasures. arXiv preprint arXiv:1711.01815 (2017)

  12. Iofciu, T., Fankhauser, P., Abel, F., Bischoff, K.: Identifying users across social tagging systems. In: ICWSM (2011)

    Google Scholar 

  13. Ji, S., Li, W., Mittal, P., Hu, X., Beyah, R.: Secgraph: a uniform and open-source evaluation system for graph data anonymization and de-anonymization. In: USENIX Security (2015)

    Google Scholar 

  14. Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1–2), 83–97 (1955)

    Article  MathSciNet  Google Scholar 

  15. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)

    MathSciNet  Google Scholar 

  16. Liu, J., Zhang, F., Song, X., Song, Y.I., Lin, C.Y., Hon, H.W.: What’s in the name?: An unsupervised approach to link users across communities. In: WSDM (2013)

    Google Scholar 

  17. Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: Hydra: large-scale social identity linkage via heterogeneous behavior modeling. In: SIGMOD (2014)

    Google Scholar 

  18. Malhotra, A., Totti, L., Meira Jr, W., Kumaraguru, P., Almeida, V.: Studying user footprints in different online social networks. In: ASONAM (2012)

    Google Scholar 

  19. Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: IEEE S&P (2009)

    Google Scholar 

  20. Nilizadeh, S., Kapadia, A., Ahn, Y.Y.: Community-enhanced de-anonymization of online social networks. In: CCS (2014)

    Google Scholar 

  21. Nunes, A., Calado, P., Martins, B.: Resolving user identities over social networks through supervised learning and rich similarity features. In: SAC (2012)

    Google Scholar 

  22. Pedarsani, P., Figueiredo, D.R., Grossglauser, M.: A bayesian method for matching two similar graphs without seeds. In: Allerton (2013)

    Google Scholar 

  23. Sharad, K., Danezis, G.: An automated social graph de-anonymization technique. In: WPES (2014)

    Google Scholar 

  24. Shu, K., Wang, S., Tang, J., Zafarani, R., Liu, H.: User identity linkage across online social networks: a review. ACM SIGKDD Explor. Newslett. 18(2), 5–17 (2017)

    Article  Google Scholar 

  25. Vosecky, J., Hong, D., Shen, V.Y.: User identification across multiple social networks. In: NDT (2009)

    Google Scholar 

  26. Wang, Y., Feng, C., Chen, L., Yin, H., Guo, C., Chu, Y.: User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web 22(6), 2611–2632 (2018). https://doi.org/10.1007/s11280-018-0572-3

    Article  Google Scholar 

  27. Wondracek, G., Holz, T., Kirda, E., Kruegel, C.: A practical attack to de-anonymize social network users. In: IEEE S&P (2010)

    Google Scholar 

  28. Zafarani, R., Liu, H.: Social computing data repository at ASU (2009). http://socialcomputing.asu.edu

  29. Zafarani, R., Liu, H.: Connecting users across social media sites: a behavioral-modeling approach. In: KDD (2013)

    Google Scholar 

  30. Zhou, J., Fan, J.: Translink: User identity linkage across heterogeneous social networks via translating embeddings. In: INFOCOM, pp. 2116–2124 (2019)

    Google Scholar 

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Acknowledgments

We thank Volkan Küçük for collecting \(\mathrm {D1}\) and \(\mathrm {D2}\) and for his help in the initial phases of this work.

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Authors and Affiliations

  1. Case Western Reserve University, Cleveland, OH, USA

    Anisa Halimi & Erman Ayday

  2. Bilkent University, Ankara, Turkey

    Erman Ayday

Authors
  1. Anisa Halimi
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  2. Erman Ayday
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Correspondence to Erman Ayday .

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Editors and Affiliations

  1. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  2. Hamburg University of Technology, Hamburg, Germany

    Dieter Gollmann

  3. Technical University of Denmark, Kongens Lyngby, Denmark

    Christian D. Jensen

  4. Singapore University of Technology and Design, Singapore, Singapore

    Jianying Zhou

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Halimi, A., Ayday, E. (2020). Profile Matching Across Online Social Networks. In: Meng, W., Gollmann, D., Jensen, C.D., Zhou, J. (eds) Information and Communications Security. ICICS 2020. Lecture Notes in Computer Science(), vol 12282. Springer, Cham. https://doi.org/10.1007/978-3-030-61078-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-61078-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61077-7

  • Online ISBN: 978-3-030-61078-4

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