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Diversity, Homophily and the Risk of Node Re-identification in Labeled Social Graphs

  • Sameera Horawalavithana
  • Clayton Gandy
  • Juan Arroyo Flores
  • John Skvoretz
  • Adriana Iamnitchi
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

Real network datasets provide significant benefits for understanding phenomena such as information diffusion or network evolution. Yet the privacy risks raised from sharing real graph datasets, even when stripped of user identity information, are significant. When nodes have associated attributes, the privacy risks increase. In this paper we quantitatively study the impact of binary node attributes on node privacy by employing machine-learning-based re-identification attacks and exploring the interplay between graph topology and attribute placement. Our experiments show that the population’s diversity on the binary attribute consistently degrades anonymity.

Notes

Acknowledgement

This research was supported by the U.S. National Science Foundation under Grant IIS 1546453.

References

  1. 1.
    Adamic, L.A., Glance, N.: The political blogosphere and the 2004 us election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 36–43. ACM (2005)Google Scholar
  2. 2.
    Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: Proceedings of the 16th International Conference on World Wide Web, pp. 181–190. ACM (2007)Google Scholar
  3. 3.
    Blackburn, J., Kourtellis, N., Skvoretz, J., Ripeanu, M., Iamnitchi, A.: Cheating in online games: a social network perspective. ACM Trans. Internet Technol. 13(3), 9:1–9:25 (2014)Google Scholar
  4. 4.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Int. Res. 16(1), 321–357 (2002)Google Scholar
  5. 5.
    Gong, N.Z., Talwalkar, A., Mackey, L., Huang, L., Shin, E.C.R., Stefanov, E., Shi, E.R., Song, D.: Joint link prediction and attribute inference using a social-attribute network. ACM Trans. Intell. Syst. Technol. 5(2), 27 (2014)CrossRefGoogle Scholar
  6. 6.
    Griffith, V., Jakobsson, M.: Messin’with texas deriving mother’s maiden names using public records. In: Applied Cryptography and Network Security, pp. 91–103. Springer (2005)Google Scholar
  7. 7.
    Haas, P.J.: Data-stream sampling: basic techniques and results. In: Data Stream Management, pp. 13–44. Springer (2016)Google Scholar
  8. 8.
    Handcock, M., Hunter, D.R., Butts, C.T., Goodreau, S., Krivitsky, P., Bender-deMoll, S., Morris, M.: statnet: software tools for the statistical analysis of network data. The Statnet Project (http://www.statnet.org). R package version (2014)
  9. 9.
    Holland, P.W., Leinhardt, S.: An exponential family of probability distributions for directed graphs. J. Am. Stat. Assoc. 76, 33–50 (1981)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Horawalavithana, S., Gandy, C., Flores, J.A., Skvoretz, J., Iamnitchi, A.: Diversity, topology, and the risk of node re-identification in labeled social graphs (2018). arXiv:1808.10837
  11. 11.
    Ji, S., Mittal, P., Beyah, R.: Graph data anonymization, de-anonymization attacks, and de-anonymizability quantification: a survey. In: IEEE Communications Surveys and Tutorials (2016)Google Scholar
  12. 12.
    Ji, S., Wang, T., Chen, J., Li, W., Mittal, P., Beyah, R.: De-sag: on the de-anonymization of structure-attribute graph data. IEEE Trans. Dependable Secur. Comput. PP(99), 1–1 (2017).  https://doi.org/10.1109/TDSC.2017.2712150
  13. 13.
    Lemos, R.: Researchers reverse netflix anonymization, securityfocus, 2007-12-04. http://www.securityfocus.com/news/11497 (2007). Accessed 11 Aug 2017
  14. 14.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web (TWEB) 1(1), 5 (2007)CrossRefGoogle Scholar
  15. 15.
    McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)Google Scholar
  16. 16.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE (2009)Google Scholar
  17. 17.
    Qian, J., Li, X.Y., Zhang, C., Chen, L.: De-anonymizing social networks and inferring private attributes using knowledge graphs. In: INFOCOM The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)Google Scholar
  18. 18.
    Sharad, K., Danezis, G.: An automated social graph de-anonymization technique. In: Proceedings of the 13th Workshop on Privacy in the Electronic Society, pp. 47–58. ACM (2014)Google Scholar
  19. 19.
    Skvoretz, J.: Diversity, integration, and social ties: attraction versus repulsion as drivers of intra- and intergroup relations. Am. J. Sociol. 119, 486–517 (2013)CrossRefGoogle Scholar
  20. 20.
    Srivatsa, M., Hicks, M.: Deanonymizing mobility traces: using social network as a side-channel. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 628–637. ACM (2012)Google Scholar
  21. 21.
    Traud, A.L., Mucha, P.J., Porter, M.A.: Social structure of facebook networks. Phys. A: Stat. Mech. Appl. 391(16), 4165–4180 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sameera Horawalavithana
    • 1
  • Clayton Gandy
    • 1
  • Juan Arroyo Flores
    • 2
  • John Skvoretz
    • 2
  • Adriana Iamnitchi
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  2. 2.Department of SociologyUniversity of South FloridaTampaUSA

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