A Semi-supervised Approach to Measuring User Privacy in Online Social Networks

  • Ruggero G. PensaEmail author
  • Gianpiero Di Blasi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9956)


During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.


Privacy metrics Active learning Online social networks 



The work presented in this paper has been co-funded by Fondazione CRT (grant number 2015-1638). The authors wish to thank all the volunteers who participated in the survey.


  1. 1.
    Akcora, C.G., Carminati, B., Ferrari, E.: Privacy in social networks: How risky is your social graph? In: Proceedings of ICDE 2012, pp. 9–19 (2012)Google Scholar
  2. 2.
    Akcora, C.G., Carminati, B., Ferrari, E.: Risks of friendships on social networks. In: Proceedings of ICDM 2012, pp. 810–815 (2012)Google Scholar
  3. 3.
    Backstrom, L., Dwork, C., Kleinberg, J.M.: Wherefore art thou R3579X?: anonymized social networks, hidden patterns, and structural steganography. Commun. ACM 54(12), 133–141 (2011)CrossRefGoogle Scholar
  4. 4.
    Becker, J., Chen, H.: Measuring privacy risk in online social networks. In: Proceedings of Web 2.0 Security and Privacy (W2SP) (2009)Google Scholar
  5. 5.
    Cavoukian, A.: Privacy by design [leading edge]. IEEE Technol. Soc. Mag. 31(4), 18–19 (2012)CrossRefGoogle Scholar
  6. 6.
    Cetto, A., Netter, M., Pernul, G., Richthammer, C., Riesner, M., Roth, C., Sänger, J.: Friend inspector: A serious game to enhance privacy awareness in social networks. In: Proceedings of IDGEI 2014 (2014)Google Scholar
  7. 7.
    Coscia, M., Rossetti, G., Giannotti, F., Pedreschi, D.: Uncovering hierarchical and overlapping communities with a local-first approach. TKDD 9(1), 6:1–6:27 (2014)CrossRefGoogle Scholar
  8. 8.
    Culotta, A., McCallum, A.: Reducing labeling effort for structured prediction tasks. In: Proceedings of AAAI 2005, pp. 746–751 (2005)Google Scholar
  9. 9.
    Dagan, I., Engelson, S.P.: Committee-based sampling for training probabilistic classifiers. In: Proceedings of ICML 1995, pp. 150–157 (1995)Google Scholar
  10. 10.
    Dunbar, R.I.M.: Do online social media cut through the constraints that limit the size of offline social networks? Roy. Soc. Open Sci. 3(1), 50292 (2016)Google Scholar
  11. 11.
    Fang, L., LeFevre, K.: Privacy wizards for social networking sites. In: Proceedings of WWW 2010 (2010)Google Scholar
  12. 12.
    Ghazinour, K., Matwin, S., Sokolova, M.: Monitoring and recommending privacy settings in social networks. In: Proceedings of 2013 EDBT/ICDT Workshop, pp. 164–168 (2013)Google Scholar
  13. 13.
    Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of SIGKDD 2003, pp. 137–146 (2003)Google Scholar
  14. 14.
    Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. PNAS 110(15), 5802–5805 (2013)CrossRefGoogle Scholar
  15. 15.
    Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of SIGIR 1994, pp. 3–12 (1994)Google Scholar
  16. 16.
    Liu, K., Terzi, E.: A framework for computing the privacy scores of users in online social networks. TKDD 5(1), 6 (2010)CrossRefGoogle Scholar
  17. 17.
    Liu, Y., Gummadi, P.K., Krishnamurthy, B., Mislove, A.: Analyzing facebook privacy settings: user expectations vs. reality. In: Proceedings of SIGCOMM IMC 2011, pp. 61–70 (2011)Google Scholar
  18. 18.
    Mislove, A., Viswanath, B., Gummadi, P.K., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of WSDM 2010, pp. 251–260 (2010)Google Scholar
  19. 19.
    Mitchell, T.M.: Machine learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  20. 20.
    Roberts, S.G.B., Dunbar, R.I.M., Pollet, T.V., Kuppens, T.: Exploring variation in active network size: Constraints and ego characteristics. Soc. Netw. 31(2), 138–146 (2009)CrossRefGoogle Scholar
  21. 21.
    Scheffer, T., Decomain, C., Wrobel, S.: Active hidden markov models for information extraction. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds.) IDA 1999. LNCS, vol. 1642, pp. 309–318. Springer, Heidelberg (2001). doi: 10.1007/3-540-44816-0_31 CrossRefGoogle Scholar
  22. 22.
    Talukder, N., Ouzzani, M., Elmagarmid, A.K., Elmeleegy, H., Yakout, M.: Privometer: Privacy protection in social networks. In: Proceedings of M3SN 2010, pp. 266–269 (2010)Google Scholar
  23. 23.
    Wang, Y., Nepali, R.K., Nikolai, J.: Social network privacy measurement and simulation. In: Proceedings of ICNC 2014, pp. 802–806 (2014)Google Scholar
  24. 24.
    Wu, L., Majedi, M., Ghazinour, K., Barker, K.: Analysis of social networking privacy policies. In: Proceedings of 2010 EDBT/ICDT Workshops (2010)Google Scholar
  25. 25.
    Zheleva, E., Getoor, L.: Privacy in social networks: A survey. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 277–306. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TorinoTurinItaly

Personalised recommendations