Advertisement

Privacy Inference Analysis on Event-Based Social Networks

  • Cailing Dong
  • Bin ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)

Abstract

In this paper, we provide a comprehensive study of privacy threats by bridging user’s online and offline social activities. We adopt the recently emerged Event-Based Social Networks (EBSNs) such as Meetup as an example. Due to the intrinsic interrelated nature of users’ online and offline social activities, our research revealed that using several simple yet effective privacy inference models, user’s privacy of online group membership and offline event attendance can be inferred with high accuracy. The level of privacy threats by bridging user’s online and offline social activities is remarkably severe.

Keywords

Online Soical Networks (OSNs) Privacy attack Privacy inference 

References

  1. 1.
    Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM 2006: Workshop on Link Analysis, Counter-terrorism and Security (2006)Google Scholar
  2. 2.
    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, pp. 635–644. ACM (2011)Google Scholar
  3. 3.
    Becker, L., Pousttchi, K.: Social networks: The role of users’ privacy concerns. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications and Services, pp. 187–195. IIWAS 2012, ACM (2012)Google Scholar
  4. 4.
    Carbunar, B., Rahman, M., Pissinou, N.: A survey of privacy vulnerabilities and defenses in geosocial networks. Commun. Mag. IEEE 51(11), 114–119 (2013)CrossRefGoogle Scholar
  5. 5.
    Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 759–768. ACM (2010)Google Scholar
  6. 6.
    Doppa, J.R., Yu, J., Tadepalli, P., Getoor, L.: Learning algorithms for link prediction based on chance constraints. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 344–360. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15880-3_28 CrossRefGoogle Scholar
  7. 7.
    Freni, D., Ruiz Vicente, C., Mascetti, S., Bettini, C., Jensen, C.S.: Preserving location and absence privacy in geo-social networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 309–318. ACM (2010)Google Scholar
  8. 8.
    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. (TIST) 5(2), 27 (2014)Google Scholar
  9. 9.
    He, J., Chu, W.W.: Protecting private information in online social networks. In: Chen, H., Yang, C.C. (eds.) Intelligence and Security Informatics. SCI, vol. 135. Springer, Heidelberg (2008)Google Scholar
  10. 10.
    He, J., Chu, W.W., Liu, Z.V.: Inferring privacy information from social networks. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, F.-Y. (eds.) ISI 2006. LNCS, vol. 3975, pp. 154–165. Springer, Heidelberg (2006). doi: 10.1007/11760146_14 CrossRefGoogle Scholar
  11. 11.
    Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Preventing private information inference attacks on social networks. Knowl. Data Eng. IEEE Trans. 25(8), 1849–1862 (2013)CrossRefGoogle Scholar
  12. 12.
    Hoens, T.R., Blanton, M., Chawla, N.V.: A private and reliable recommendation system for social networks. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 816–825. IEEE (2010)Google Scholar
  13. 13.
    Huo, Z., Meng, X., Zhang, R.: Feel free to check-in: privacy alert against hidden location inference attacks in GeoSNs. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7825, pp. 377–391. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37487-6_29 CrossRefGoogle Scholar
  14. 14.
    Jorgensen, Z., Yu, T.: A privacy-preserving framework for personalized, social recommendations. In: EDBT, pp. 571–582 (2014)Google Scholar
  15. 15.
    Jurgens, D.: That’s what friends are for: Inferring location in online social media platforms based on social relationships. In: ICWSM (2013)Google Scholar
  16. 16.
    Li, D., Lv, Q., Shang, L., Gu, N.: Yana: an efficient privacy-preserving recommender system for online social communities. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2269–2272. ACM (2011)Google Scholar
  17. 17.
    Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–252. ACM (2010)Google Scholar
  18. 18.
    Liu, B., Xiong, H.: Point-of-interest recommendation in location based social networks with topic and location awareness. In: SDM, pp. 396–404. SIAM (2013)Google Scholar
  19. 19.
    Liu, X., He, Q., Tian, Y., Lee, W.C., McPherson, J., Han, J.: Event-based social networks: Linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1032–1040. ACM, New York (2012)Google Scholar
  20. 20.
    Mahmud, J., Nichols, J., Drews, C.: Where is this tweet from? inferring home locations of twitter users. In: ICWSM (2012)Google Scholar
  21. 21.
    Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 251–260. ACM (2010)Google Scholar
  22. 22.
    Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1046–1054. ACM (2011)Google Scholar
  23. 23.
    Shang, S., Hui, Y., Hui, P., Cuff, P., Kulkarni, S.: Privacy preserving recommendation system based on groups. arXiv preprint (2013). arXiv:1305.0540
  24. 24.
    Terrovitis, M.: Privacy preservation in the dissemination of location data. ACM SIGKDD Explor. Newsl. 13(1), 6–18 (2011)CrossRefGoogle Scholar
  25. 25.
    Xu, D., Cui, P., Zhu, W., Yang, S.: Graph-based residence location inference for social media users. MultiMedia IEEE 21(4), 76–83 (2014)CrossRefGoogle Scholar
  26. 26.
    Yang, S.H., Long, B., Smola, A., Sadagopan, N., Zheng, Z., Zha, H.: Like like alike: Joint friendship and interest propagation in social networks. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 537–546. ACM (2011)Google Scholar
  27. 27.
    Zheleva, E., Getoor, L.: To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In: Proceedings of the 18th International Conference on World Wide Web, pp. 531–540. ACM (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Information SystemsUniversity of MarylandBaltimoreUSA

Personalised recommendations