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)


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.


Crowdsourcing Trust Spam worker identification 


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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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