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N2TM: A New Node to Trust Matrix Method for Spam Worker Defense in Crowdsourcing Environments

  • Bin Ye
  • Yan WangEmail author
  • Mehmet Orgun
  • Quan Z. Sheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)

Abstract

To defend against spam workers in crowdsourcing environments, the existing solutions overlook the fact that a spam worker with guises can easily bypass the defense. To alleviate this problem, in this paper, we propose a Node to Trust Matrix method (N2TM) that represents a worker node in a crowdsourcing network as an un-manipulable Worker Trust Matrix (WTM) for identifying the worker’s identity. In particular, we first present a crowdsourcing trust network consisting of requester nodes, worker nodes, and transaction-based edges. Then, we construct WTMs for workers based on the trust network. A WTM consists of trust indicators measuring the extent to which a worker is trusted by different requesters in different sub-networks. Moreover, we show the un-manipulable property and the usable property of a WTM that are crucial for identifying a worker’s identity. Furthermore, we leverage deep learning techniques to predict a worker’s identity with its WTM as input. Finally, we demonstrate the superior performance of our proposed N2TM in identifying spam workers with extensive experiments.

Keywords

Crowdsourcing Trust Spam worker identification 

References

  1. 1.
    Allahbakhsh, M., Ignjatovic, A., Benatallah, B., Beheshti, S., Bertino, E., Foo, N.: Reputation management in crowdsourcing systems. In: Proceeding of the 2012 International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp. 664–671 (2012).  https://doi.org/10.4108/icst.collaboratecom.2012.250499
  2. 2.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), 10008 (2008)CrossRefGoogle Scholar
  3. 3.
    Callison-Burch, C., Dredze, M.: Creating speech and language data with Amazon’s mechanical turk. In: Proceedings of the 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, USA, pp. 1–12 (2010). https://aclanthology.info/papers/W10-0701/w10-0701
  4. 4.
    Cao, Q., Sirivianos, M., Yang, X., Pregueiro, T.: Aiding the detection of fake accounts in large scale social online services. In: Proceedings of the 2012 USENIX Symposium on Networked Systems Design and Implementation, NSDI 2012, San Jose, CA, USA, pp. 197–210 (2012), https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/cao
  5. 5.
    Danezis, G., Mittal, P.: Sybilinfer: Detecting sybil nodes using social networks. In: Proceedings of the 2009 Network and Distributed System Security Symposium, NDSS, San Diego, California, USA (2009). http://www.isoc.org/isoc/conferences/ndss/09/pdf/06.pdf
  6. 6.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, pp. 403–412. ACM (2004)Google Scholar
  7. 7.
    Jagabathula, S., Subramanian, L., Venkataraman, A.: Reputation-based worker filtering in crowdsourcing. In: Proceeding of the 2014 Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, pp. 2492–2500 (2014). http://papers.nips.cc/paper/5393-reputation-based-worker-filtering-in-crowdsourcing
  8. 8.
    Jeff, H.: The rise of crowdsourcing. Wired Mag. 14(6), 1–4 (2006)Google Scholar
  9. 9.
    Karger, D.R., Oh, S., Shah, D.: Iterative learning for reliable crowdsourcing systems. In: Proceeding of the 2011 Annual Conference on Neural Information Processing Systems, Granada, Spain, pp. 1953–1961 (2011). http://papers.nips.cc/paper/4396-iterative-learning-for-reliable-crowdsourcing-systems
  10. 10.
    KhudaBukhsh, A.R., Carbonell, J.G., Jansen, P.J.: Detecting non-adversarial collusion in crowdsourcing. In: Proceedings of the 2014 Second AAAI Conference on Human Computation and Crowdsourcing, HCOMP, Pittsburgh, Pennsylvania, USA (2014). http://www.aaai.org/ocs/index.php/HCOMP/HCOMP14/paper/view/8967
  11. 11.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  12. 12.
    Liu, X., Lu, M., Ooi, B.C., Shen, Y., Wu, S., Zhang, M.: Cdas: A crowdsourcing data analytics system. PVLDB 5(10), 1040–1051 (2012). http://vldb.org/pvldb/vol5/p1040xuanliuvldb2012.pdfGoogle Scholar
  13. 13.
    Mashhadi, A.J., Capra, L.: Quality control for real-time ubiquitous crowdsourcing. In: Proceedings of the 2011 International Workshop on Ubiquitous Crowdsouring, pp. 5–8. ACM (2011)Google Scholar
  14. 14.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781
  15. 15.
    Peer, E., Vosgerau, J., Acquisti, A.: Reputation as a sufficient condition for data quality on Amazon mechanical turk. Behav. Res. Methods 46(4), 1023–1031 (2014)CrossRefGoogle Scholar
  16. 16.
    Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: The Proceeding of 2014 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA, pp. 701–710 (2014).  https://doi.org/10.1145/2623330.2623732
  17. 17.
    Raykar, V.C., Yu, S.: Eliminating spammers and ranking annotators for crowdsourced labeling tasks. J. Mach. Learn. Res. 13, 491–518 (2012). http://dl.acm.org/citation.cfm?id=2188401MathSciNetzbMATHGoogle Scholar
  18. 18.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRefGoogle Scholar
  19. 19.
    Stefanovitch, N., Alshamsi, A., Cebrian, M., Rahwan, I.: Error and attack tolerance of collective problem solving: the darpa shredder challenge. EPJ Data Sci. 3(1), 13 (2014)CrossRefGoogle Scholar
  20. 20.
    Tran, D.N., Min, B., Li, J., Subramanian, L.: Sybil-resilient online content voting. In: Proceedings of the 2009 USENIX Symposium on NSDI, Boston, MA, USA, pp. 15–28 (2009). http://www.usenix.org/events/nsdi09/tech/full_papers/tran/tran.pdf
  21. 21.
    Vuurens, J.B.P., de Vries, A.P.: Obtaining high-quality relevance judgments using crowdsourcing. IEEE Internet Comput. 16(5), 20–27 (2012).  https://doi.org/10.1109/MIC.2012.71CrossRefGoogle Scholar
  22. 22.
    Vuurens, J.B., de Vries, A.P., Eickhoff, C.: How much spam can you take? An analysis of crowdsourcing results to increase accuracy. In: ACM SIGIR Workshop on Crowdsourcing for Information Retrieval, CIR11, pp. 21–26 (2011)Google Scholar
  23. 23.
    Wei, W., Xu, F., Tan, C.C., Li, Q.: Sybildefender: a defense mechanism forsybil attacks in large social networks. IEEE Trans. Parallel Distrib. Syst. 24(12), 2492–2502 (2013).  https://doi.org/10.1109/TPDS.2013.9CrossRefGoogle Scholar
  24. 24.
    Ye, B., Wang, Y., Liu, L.: Crowd trust: a context-aware trust model for worker selection in crowdsourcing environments. In: Proceeding of the 2015 IEEE International Conference on Web Services, ICWS 2015, New York, NY, USA, pp. 121–128 (2015).  https://doi.org/10.1109/ICWS.2015.26
  25. 25.
    Ye, B., Wang, Y., Liu, L.: Crowddefense: a trust vector-based threat defense model in crowdsourcing environments. In: Proceeding of the 2017 IEEE International Conference on Web Services, ICWS 2017, Honolulu, HI, USA, pp. 245–252 (2017).  https://doi.org/10.1109/ICWS.2017.39
  26. 26.
    Yu, H., Gibbons, P.B., Kaminsky, M., Xiao, F.: Sybillimit: a near-optimal social network defense against sybil attacks. IEEE/ACM Trans. Netw. 18(3), 885–898 (2010).  https://doi.org/10.1109/TNET.2009.2034047CrossRefGoogle Scholar
  27. 27.
    Yu, H., Shen, Z., Miao, C., An, B.: Challenges and opportunities for trust management in crowdsourcing. In: Proceeding of the 2012 IEEE/WIC/ACM International Conferences on Intelligent Agent Technology, IAT 2012, Macau, China, pp. 486–493 (2012).  https://doi.org/10.1109/WI-IAT.2012.104
  28. 28.
    Yuen, M., King, I., Leung, K.: A survey of crowdsourcing systems. In: 2011 IEEE Conference on Privacy, Security, Risk and Trust (PASSAT) and on Social Computing (SocialCom), Boston, MA, USA, pp. 766–773 (2011). https://doi.org/10.1109/PASSAT/SocialCom.2011.203

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bin Ye
    • 1
  • Yan Wang
    • 1
    Email author
  • Mehmet Orgun
    • 1
  • Quan Z. Sheng
    • 1
  1. 1.Macquarie UniversitySydneyAustralia

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