A sybil-resistant truth discovery framework for mobile crowdsensing

Jian Lin, Dejun Yang, Kun Wu, Jian Tang, Guoliang Xue

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

Abstract

The rapid proliferation of sensor-embedded devices has enabled the mobile crowdsensing (MCS), a new paradigm which effectively collects sensing data from pervasive users. In order to identify the true information from the noisy data submitted by unreliable users, truth discovery algorithms have been proposed for the MCS systems to aggregate data. However, the power of truth discovery algorithms will be undermined by the Sybil attack, in which an attacker can benefit from using multiple accounts. In addition, an MCS system will be jeopardized unless it is resistant to the Sybil attack. In this paper, we proposed a Sybil-resistant truth discovery framework for MCS, which ensures high accuracy under the Sybil attack. To diminish the impact of the Sybil attack, we design three account grouping methods for the framework, which are used in pair with a truth discovery algorithm. We evaluate the proposed framework through a real-world experiment. The results show that existing truth discovery algorithms are vulnerable to the Sybil attack, and the proposed framework can effectively diminish the impact of the Sybil attack.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages871-880
Number of pages10
ISBN (Electronic)9781728125190
DOIs
StatePublished - Jul 2019
Event39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 - Richardson, United States
Duration: Jul 7 2019Jul 9 2019

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2019-July

Conference

Conference39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
CountryUnited States
CityRichardson
Period7/7/197/9/19

Fingerprint

Sensors
Experiments

Keywords

  • Mobile crowdsensing
  • Sybil-Resistant
  • Truth Discovery

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Lin, J., Yang, D., Wu, K., Tang, J., & Xue, G. (2019). A sybil-resistant truth discovery framework for mobile crowdsensing. In Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 (pp. 871-880). [8884822] (Proceedings - International Conference on Distributed Computing Systems; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2019.00091

A sybil-resistant truth discovery framework for mobile crowdsensing. / Lin, Jian; Yang, Dejun; Wu, Kun; Tang, Jian; Xue, Guoliang.

Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 871-880 8884822 (Proceedings - International Conference on Distributed Computing Systems; Vol. 2019-July).

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

Lin, J, Yang, D, Wu, K, Tang, J & Xue, G 2019, A sybil-resistant truth discovery framework for mobile crowdsensing. in Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019., 8884822, Proceedings - International Conference on Distributed Computing Systems, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., pp. 871-880, 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019, Richardson, United States, 7/7/19. https://doi.org/10.1109/ICDCS.2019.00091
Lin J, Yang D, Wu K, Tang J, Xue G. A sybil-resistant truth discovery framework for mobile crowdsensing. In Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 871-880. 8884822. (Proceedings - International Conference on Distributed Computing Systems). https://doi.org/10.1109/ICDCS.2019.00091
Lin, Jian ; Yang, Dejun ; Wu, Kun ; Tang, Jian ; Xue, Guoliang. / A sybil-resistant truth discovery framework for mobile crowdsensing. Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 871-880 (Proceedings - International Conference on Distributed Computing Systems).
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