CADIVa: cooperative and adaptive decentralized identity validation model for social networks

  • Amira SolimanEmail author
  • Leila Bahri
  • Sarunas Girdzijauskas
  • Barbara Carminati
  • Elena Ferrari
Original Article


Online social networks (OSNs) have successfully changed the way people interact. Online interactions among people span geographical boundaries and interweave with different human life activities. However, current OSNs identification schemes lack guarantees on quantifying the trustworthiness of online identities of users joining them. Therefore, driven from the need to empower users with an identity validation scheme, we introduce a novel model, cooperative and adaptive decentralized identity validation CADIVa, that allows OSN users to assign trust levels to whomever they interact with. CADIVa exploits association rule mining approach to extract the identity correlations among profile attributes in every individual community in a social network. CADIVa is a fully decentralized and adaptive model that exploits fully decentralized learning and cooperative approaches not only to preserve users privacy, but also to increase the system reliability and to make it resilient to mono-failure. CADIVa follows the ensemble learning paradigm to preserve users privacy and employs gossip protocols to achieve efficient and low-overhead communication. We provide two different implementation scenarios of CADIVa. Results confirm CADIVa’s ability to provide fine-grained community-aware identity validation with average improvement up to 36 and 50 % compared to the semi-centralized or global approaches, respectively.


Identity validation Online social networks Distributed systems Privacy preservation Decentralized online social networks 


  1. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: ACM SIGMOD record, vol 22. ACM, pp 207–216Google Scholar
  2. Agrawal R, Srikant R, et al (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large data bases, VLDB, vol 1215, pp 487–499Google Scholar
  3. Akcora CG, Carminati B, Ferrari E (2012) Privacy in social networks: how risky is your social graph? In: ICDE’12. IEEE, pp 9–19Google Scholar
  4. Bahri L, Carminati B, Ferrari E (2014) Community-based identity validation on online social networks. In: IEEE 34th international conference on distributed computing systems (ICDCS), 2014. IEEE, pp 21–30Google Scholar
  5. Brandes U, Delcling D, Gaertler M, Gorke R, Hoefer M, Nikoloski Z, Wagner D (2008) On modularity clustering. IEEE Trans Knowl Data Eng 20(2):172–188CrossRefGoogle Scholar
  6. Cai X, Bain M, Krzywicki A, Wobcke W, Kim YS, Compton P, Mahidadia A (2011) Collaborative filtering for people to people recommendation in social networks. In: AI 2010: advances in artificial intelligence. Springer, pp 476–485Google Scholar
  7. Chairunnanda P, Pham N, Hengartner U (2011) Privacy: Gone with the typing! identifying web users by their typing patterns. In: IEEE third international conference on privacy, security, risk and trust (passat), 2011 and 2011 IEEE third international conference on social computing (socialcom). IEEE, pp 974–980Google Scholar
  8. Chorley M (2012) How facebook and social networking sites are used by child abuse gangs to groom victims for ‘sex parties’. Available at: Accessed 12 June 2016
  9. Datta A, Buchegger S, Vu L-H, Strufe T, Rzadca K (2010) Decentralized online social networks. In: Handbook of social network technologies and applications. Springer, pp 349–378Google Scholar
  10. Debatin B, Lovejoy JP, Horn A-K, Hughes BN (2009) Facebook and online privacy: attitudes, behaviors, and unintended consequences. J Comput Mediat Commun 15(1):83–108CrossRefGoogle Scholar
  11. Dwyer C (2011) Privacy in the age of google and facebook. Technol Soc Mag IEEE 30(3):58–63CrossRefGoogle Scholar
  12. Ferrara E (2012) Community structure discovery in facebook. Int J Soc Netw Min 1:67–90CrossRefGoogle Scholar
  13. Goga O, Lei H, Parthasarathi SHK, Friedland G, Sommer R, Teixeira R (2013) Exploiting innocuous activity for correlating users across sites. In: Proceedings of the 22nd international conference on World Wide Web. International World Wide Web conferences steering committee, pp 447–458Google Scholar
  14. Gong NZ, Talwalkar A, Mackey L, Huang L, Shin ECR, Stefanov E, Song D, et al. (2011) Jointly predicting links and inferring attributes using a social-attribute network (san). arXiv preprint arXiv:1112.3265
  15. He B-Z, Chen C-M, Su Y-P, Sun H-M (2014) A defence scheme against identity theft attack based on multiple social networks. Expert Syst Appl 41(5):2345–2352CrossRefGoogle Scholar
  16. Hipp J, Güntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining a general survey and comparison. ACM Sigkdd Explor Newsl 2(1):58–64CrossRefGoogle Scholar
  17. Hope C (2013) Facebook is a ‘major location for online child sexual grooming’, head of child protection agency says. TelegrGoogle Scholar
  18. Huber M, Mulazzani M, Weippl E, Kitzler G, Goluch S (2011) Friend-in-the-middle attacks: exploiting social networking sites for spam. Internet Comput IEEE 15(3):28–34CrossRefGoogle Scholar
  19. Jagatic TN, Johnson NA, Jakobsson M, Menczer F (2007) Social phishing. Commun ACM 50(10):94–100CrossRefGoogle Scholar
  20. Jin L, Takabi H, Joshi JB (2011) Towards active detection of identity clone attacks on online social networks. In: Proceedings of the first ACM conference on data and application security and privacy. ACM, pp 27–38Google Scholar
  21. Kapanipathi P, Anaya J, Sheth A, Slatkin B, Passant A (2011) Privacy-aware and scalable content dissemination in distributed social networks. Semant Web-ISWC 2011:157–172Google Scholar
  22. Koll D, Li J, Fu X (2014) Soup: an online social network by the people, for the people. In: Proceedings of the 15th international middleware conference. ACM, pp 193–204Google Scholar
  23. Kotsiantis S, Kanellopoulos D (2006) Association rules mining: a recent overview. GESTS Int Trans Comput Sci Eng 32(1):71–82Google Scholar
  24. Krivitsky PN, Handcock MS, Raftery AE, Hoff PD (2009) Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Soc Netw 31(3):204–213CrossRefGoogle Scholar
  25. Li N, Qardaji WH, Su D (2011) Provably private data anonymization: or, k-anonymity meets differential privacy. CoRR 49:55 arXiv:1101.2604 Google Scholar
  26. Low Y, Bickson D, Gonzalez J, Guestrin C, Kyrola A, Hellerstein JM (2012) Distributed graphlab: a framework for machine learning and data mining in the cloud. Proc VLDB Endow 5:716–727CrossRefGoogle Scholar
  27. Luo W, Liu J, Liu J, Fan C (2009) An analysis of security in social networks. In: Eighth IEEE international conference on dependable, autonomic and secure computing, 2009. DASC’09. IEEE, pp 648–651Google Scholar
  28. Lynch MJ, Michalowski RJ, Groves WB (2000) The new primer in radical criminology: critical perspectives on crime, power, and identity. Criminal Justice Press, MonseyGoogle Scholar
  29. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582CrossRefGoogle Scholar
  30. Nilizadeh S, Jahid S, Mittal P, Borisov N, Kapadia A (2012) Cachet: a decentralized architecture for privacy preserving social networking with caching. In: Proceedings of the 8th international conference on emerging networking experiments and technologies. ACM, pp 337–348Google Scholar
  31. Rahimian F, Girdzijauskas S, Haridi S (2014) Parallel community detection for cross-document coreference. In: IEEE/WIC/ACM international joint conferences on web intelligence (WI) and Intelligent Agent Technologies (IAT), 2014, vol 2. IEEE, pp 46–53Google Scholar
  32. Robinson RM (2015) Social engineering attackers deploy fake social media profiles. Secur IntellGoogle Scholar
  33. Roffo G, Segalin C, Vinciarelli A, Murino V, Cristani M (2013) Reading between the turns: Statistical modeling for identity recognition and verification in chats. In: 10th IEEE international conference on advanced video and signal based surveillance (AVSS), 2013. IEEE, pp 99–104Google Scholar
  34. Sirivianos M, Kim K, Gan JW, Yang X (2012) Assessing the veracity of identity assertions via osns. In: Fourth international conference on communication systems and networks (COMSNETS), 2012. IEEE, pp 1–10Google Scholar
  35. Soliman A, Bahri L, Carminati B, Ferrari E, Girdzijauskas S (2015) Diva: decentralized identity validation for social networks. In: IEEE/ACM international conference on advances in social network analysis and mining (ASONAM), 2015. IEEE/ACM, pp 383–391Google Scholar
  36. Spears RE, Oakes PJ, Ellemers NE, Haslam S (1997) The social psychology of stereotyping and group life. Blackwell PublishingGoogle Scholar
  37. Stets JE, Burke PJ (2003) A sociological approach to self and identity. Handbook of self and identity pp 128–152Google Scholar
  38. Stringhini G (2014) Stepping up the cybersecurity game: protecting online services from malicious activity. Ph.D. thesis, University of California, Santa BarbaraGoogle Scholar
  39. Yu H, Kaminsky M, Gibbons PB, Flaxman A (2006) Sybilguard: defending against sybil attacks via social networks. ACM SIGCOMM Comput Commun Rev 36(4):267–278CrossRefGoogle Scholar
  40. Yu H, Gibbons PB, Kaminsky M, Xiao F (2008) Sybillimit: a near-optimal social network defense against sybil attacks. In: IEEE symposium on security and privacy, 2008. SP 2008. IEEE, pp 3–17Google Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Amira Soliman
    • 1
    Email author
  • Leila Bahri
    • 2
  • Sarunas Girdzijauskas
    • 1
  • Barbara Carminati
    • 2
  • Elena Ferrari
    • 2
  1. 1.Software and Computer Systems, School of Information and Communication TechnologyRoyal Institute of Technology (KTH)StockholmSweden
  2. 2.DISTAInsubria UniversityVareseItaly

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