Iron Mask: Trust-Preserving Anonymity on the Face of Stigmatization in Social Networking Sites

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10442)

Abstract

Social networking sites are pervasively being used for seeking advice, asking questions, giving answers, and sharing experiences on various topics including health. When users share content about sensitive health topics, such as sexual dysfunction, infertility, or STDs, they may wish to do so anonymously to avoid stigmatization and the associated negative effects on mental health. However, a user masking their name with a pseudonym may still be inadvertently exposing their identity because of various quasi-identifiers present in their profile. One such quasi-identifier that has not been investigated in literature is the content itself, which could be used for authorship identification. Moreover, an anonymous user’s credibility cannot be established because their profile is no longer linked with their reputation. This study proposes the Iron Mask algorithm for providing enhanced anonymity while preserving trust. Iron Mask improves anonymity by using a probabilistic machine learning approach based on whiteprint identification and inclusion of content as a quasi-identifier. Iron Mask also introduces the concept of a trust-preserving pseudonym which masks user identity without loss of credibility. We evaluate the proposed algorithm using datasets from Quora, a question-answering social networking site, and demonstrate the efficacy of our algorithm with satisfactory recall and survey feedback results.

Keywords

Anonymity Pseudonymity Trust 

References

  1. 1.
    Hansen, D.L., Johnson, C.: Veiled viral marketing: disseminating information on stigmatized illnesses via social networking sites. In: 2nd ACM SIGHIT International Health Informatics Symposium, pp. 247–254. ACM, Miami (2012)Google Scholar
  2. 2.
    White, M., Dorman, S.M.: Receiving social support online: implications for health education. Health Educ. Res. 16(6), 693–707 (2001)CrossRefGoogle Scholar
  3. 3.
    Frost, D.M.: Social stigma and its consequences for the socially stigmatized. Soc. Pers. Psychol. Compass 5(11), 824–839 (2011)CrossRefGoogle Scholar
  4. 4.
    Hong, Y., Patrick, T.B., Gillis, R.: Protection of patient’s privacy and data security in e-health services. In: 1st International Conference on Biomedical Engineering and Informatics, pp. 643–647. IEEE, Sanya (2008)Google Scholar
  5. 5.
    Cofta, P.: Confidence, trust and identity. BT Technol. J. 25(2), 173–178 (2007)CrossRefGoogle Scholar
  6. 6.
    Child, J.: Trust - the fundamental bond in global collaboration. Org. Dyn. 29(4), 274–288 (2001)CrossRefGoogle Scholar
  7. 7.
    Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79228-4_1 CrossRefGoogle Scholar
  8. 8.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE Computer Society, Washington, DC (2009)Google Scholar
  9. 9.
    Beach, A., Gartrell, M., Han, R.: q-Anon: rethinking anonymity for social networks. In: 2nd International Conference on Social Computing, pp. 185–192. IEEE, Minneapolis (2010)Google Scholar
  10. 10.
    Milhail, S., Ilya, L.: Methodologies of internet portals users’ short messages texts authorship identification based on the methods of mathematical linguistics. In: 8th International Conference on Application of Information and Communication Technologies, pp. 1–6. IEEE, Astana (2014)Google Scholar
  11. 11.
    Keretna, S., Hossny, A., Creighton, D.: Recognising user identity in Twitter social networks via text mining. In: International Conference on Systems, Man, and Cybernetics, pp. 3079–3082. IEEE Computer Society, Washington, DC (2013)Google Scholar
  12. 12.
    Johnson, A.M., Syverson, P., Dingledine, R., Mathewson, N.: Trust-based anonymous communication: adversary models and routing algorithms. In: 18th Conference on Computer and Communications Security, pp. 175–186. ACM, Chicago (2011)Google Scholar
  13. 13.
    Sassone, V., Hamadou, S., Yang, M.: Trust in anonymity networks. In: Gastin, P., Laroussinie, F. (eds.) CONCUR 2010. LNCS, vol. 6269, pp. 48–70. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15375-4_5 CrossRefGoogle Scholar
  14. 14.
    Maurer, F.K.: A survey on approaches to anonymity in bitcoin and other cryptocurrencies. In: Mayr, H.C., Pinzger, M. (eds.) INFORMATIK 2016. LNI, vol. 259, pp. 2145–2150. Gesellschaft für Informatik, Bonn (2016)Google Scholar
  15. 15.
    Bernstein, M.S., Monroy-Hernández, A., Harry, D., André, P., Panovich, K., Vargas, G.G.: 4chan and /b/: an analysis of anonymity and ephemerality in a large online community. In: 5th International Conference on Weblogs and Social Media, pp. 50–57. AAAI, Barcelona (2011)Google Scholar
  16. 16.
    Taskar, B., Segal, E., Koller, D.: Probabilistic classification and clustering in relational data. In: International Joint Conference on Artificial Intelligence, pp. 870–878. Lawrence Erlbaum Associates Ltd., Seattle (2001)Google Scholar
  17. 17.
    Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: 8th International Conference on Knowledge Discovery and Data Mining, pp. 694–699. ACM, New York (2002)Google Scholar
  18. 18.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATHGoogle Scholar
  19. 19.
    Kameya, Y., Hayashi, K.: Bottom-up cell suppression that preserves the missing-at-random condition. In: Katsikas, S., Lambrinoudakis, C., Furnell, S. (eds.) TrustBus 2016. LNCS, vol. 9830, pp. 65–78. Springer, Cham (2016). doi:10.1007/978-3-319-44341-6_5 CrossRefGoogle Scholar
  20. 20.
    Dreyfus, S.E., Dreyfus, H.L.: A five-stage model of the mental activities involved in directed skill acquisition. California University Berkeley Operations Research Center (1980)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of AlbertaEdmontonCanada

Personalised recommendations