Sybil-proof incentive mechanisms for crowdsensing

Jian Lin, Ming Li, Dejun Yang, Guoliang Xue, Jian Tang

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

41 Scopus citations


The rapid growth of sensor-embedded smartphones has led to a new data sensing and collecting paradigm, known as crowdsensing. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in crowdsensing. However, none of them have taken into consideration the Sybil attack where a user illegitimately pretends multiple identities to gain benefits. This attack may undermine existing inventive mechanisms. To deter the Sybil attack, we design Sybil-proof auction-based incentive mechanisms for crowdsensing in this paper. We investigate both the single-minded and multi-minded cases and propose SPIM-S and SPIM-M, respectively. SPIM-S achieves computational efficiency, individual rationality, truthfulness, and Sybil-proofness. SPIM-M achieves individual rationality, truthfulness, and Sybil-proofness. We evaluate the performance and validate the desired properties of SPIM-S and SPIM-M through extensive simulations.

Original languageEnglish (US)
Title of host publicationINFOCOM 2017 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053360
StatePublished - Oct 2 2017
Event2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States
Duration: May 1 2017May 4 2017

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Other2017 IEEE Conference on Computer Communications, INFOCOM 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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