Sensitive task assignments in crowdsourcing markets with colluding workers

Haipei Sun, Boxiang Dong, Bo Zhang, Wendy Hui Wang, Murat Kantarcioglu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Crowdsourcing has raised several security concerns. One of the concerns is how to assign sensitive tasks in the crowdsourcing market, especially when there are colluding participants in crowdsourcing. In this paper, we consider adversarial colluding participants who intend to extract sensitive data by exchanging information. We design a 3-step sensitive task assignment method: (1) the collusion estimation step that quantifies the workers' pairwise collusion probability by estimating answer truth based on their responses; (2) the worker selection step that executes a heuristic sampling-based approach to select the fewest workers whose collusion probability satisfies the given security requirement; and (3) the task partitioning step that splits the sensitive information among the selected workers. We perform an extensive set of experiments on both real-world and synthetic datasets. The results demonstrate the accuracy and efficiency of our method.

Original languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages377-388
Number of pages12
ISBN (Electronic)9781538655207
DOIs
StatePublished - 24 Oct 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018

Publication series

NameProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

Other

Other34th IEEE International Conference on Data Engineering, ICDE 2018
CountryFrance
CityParis
Period16/04/1819/04/18

Keywords

  • Collusion detection
  • Crowdsourcing
  • Task assignment

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  • Cite this

    Sun, H., Dong, B., Zhang, B., Wang, W. H., & Kantarcioglu, M. (2018). Sensitive task assignments in crowdsourcing markets with colluding workers. In Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 (pp. 377-388). [8509263] (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2018.00042