N2TM: a new node to trust matrix method for spam worker defense in crowdsourcing environments

Bin Ye, Yan Wang*, Mehmet Orgun, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationService-Oriented Computing
Subtitle of host publication17th International Conference, ICSOC 2019, Proceedings
EditorsSami Yangui, Ismael Bouassida Rodriguez, Khalil Drira, Zahir Tari
Place of PublicationSwitzerland
Number of pages16
ISBN (Electronic)9783030337025
ISBN (Print)9783030337018
Publication statusPublished - 2019
Event17th International Conference on Service-Oriented Computing, ICSOC 2019 - Toulouse, France
Duration: 28 Oct 201931 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11895 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Service-Oriented Computing, ICSOC 2019


  • Crowdsourcing
  • Spam worker identification
  • Trust


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