Identification Services for Online Social Networks (OSNs) Extended Abstract

  • Elena FerrariEmail author
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 526)


On-line Social Networks (OSNs) have dramatically changed how users connect, communicate, share content, and exchange goods and services. However, despite all the benefits and the flexibility that OSNs provide, their users become more reliant on online identities with often no means to know who really is behind an online profile. Indeed, to facilitate their adoption and encourage people to join, identities in OSNs are very loose, in that not much more than an email address is required to create an account and related profile. Therefore, the problem of fake accounts and identity related attacks in OSNs has attracted considerable interest from the research community, and resulted in several proposals that mainly aim at detecting malicious nodes that follow identified and formalized attack trends. Without denying the importance of formalizing Sybil attacks and suggesting solutions for their detection, in this extended abstract we also consider the issue of identity validation from a user perspective, by briefly discussing the research proposals aiming at empowering users with tools helping them to identify the validity of the online accounts they interact with.


  1. 1.
    Al-Quirishi, et al.: Sybil defence techniques in online social networks: a survey. IEEE Access 5, 1200–1219 (2017)CrossRefGoogle Scholar
  2. 2.
    Bahri, L., Carminati, B., Ferrari, E.: COIP - continuous, operable, impartial, and privacy-aware identity validity estimation for OSN profiles. ACM Trans. Web 10(4), 23:1–23:41 (2016)CrossRefGoogle Scholar
  3. 3.
    Kansara, K.B., Shekokar, N.M.: At a glance of sybil detection in OSN. In: Proceedings of IEEE International Symposium on Nanoelectronic and Information Systems (2015)Google Scholar
  4. 4.
    Laleh, N., Carminati, B., Ferrari, E.: Risk assessment in social networks based on user anomalous behaviour. IEEE Trans. Dependable Secur. Comput. 15, 295–308 (2016)CrossRefGoogle Scholar
  5. 5.
    Li, Y., Martinez, O., Chen, X., Li, Y., Hopcroft, J.E.: In a world that counts: clustering and detecting fake social engagement at scale. In: Proceedings of the 25th International Conference on World Wide Web (2016)Google Scholar
  6. 6.
    Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B.Y., Dai, Y.: Uncovering social network sybils in the wild. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 2 (2014)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Department of Theoretical and Applied SciencesUniversity of InsubriaVareseItaly

Personalised recommendations