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

8 Citations (Scopus)

Abstract

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
DOIs
StatePublished - Oct 2 2017
Event2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States
Duration: May 1 2017May 4 2017

Other

Other2017 IEEE Conference on Computer Communications, INFOCOM 2017
CountryUnited States
CityAtlanta
Period5/1/175/4/17

Fingerprint

Smartphones
Computational efficiency
Sensors

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Lin, J., Li, M., Yang, D., Xue, G., & Tang, J. (2017). Sybil-proof incentive mechanisms for crowdsensing. In INFOCOM 2017 - IEEE Conference on Computer Communications [8057175] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2017.8057175

Sybil-proof incentive mechanisms for crowdsensing. / Lin, Jian; Li, Ming; Yang, Dejun; Xue, Guoliang; Tang, Jian.

INFOCOM 2017 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2017. 8057175.

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

Lin, J, Li, M, Yang, D, Xue, G & Tang, J 2017, Sybil-proof incentive mechanisms for crowdsensing. in INFOCOM 2017 - IEEE Conference on Computer Communications., 8057175, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Conference on Computer Communications, INFOCOM 2017, Atlanta, United States, 5/1/17. https://doi.org/10.1109/INFOCOM.2017.8057175
Lin J, Li M, Yang D, Xue G, Tang J. Sybil-proof incentive mechanisms for crowdsensing. In INFOCOM 2017 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc. 2017. 8057175 https://doi.org/10.1109/INFOCOM.2017.8057175
Lin, Jian ; Li, Ming ; Yang, Dejun ; Xue, Guoliang ; Tang, Jian. / Sybil-proof incentive mechanisms for crowdsensing. INFOCOM 2017 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2017.
@inproceedings{044414c290014292a722b8a1a30d8430,
title = "Sybil-proof incentive mechanisms for crowdsensing",
abstract = "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.",
author = "Jian Lin and Ming Li and Dejun Yang and Guoliang Xue and Jian Tang",
year = "2017",
month = "10",
day = "2",
doi = "10.1109/INFOCOM.2017.8057175",
language = "English (US)",
booktitle = "INFOCOM 2017 - IEEE Conference on Computer Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Sybil-proof incentive mechanisms for crowdsensing

AU - Lin, Jian

AU - Li, Ming

AU - Yang, Dejun

AU - Xue, Guoliang

AU - Tang, Jian

PY - 2017/10/2

Y1 - 2017/10/2

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85034080489&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85034080489&partnerID=8YFLogxK

U2 - 10.1109/INFOCOM.2017.8057175

DO - 10.1109/INFOCOM.2017.8057175

M3 - Conference contribution

BT - INFOCOM 2017 - IEEE Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers Inc.

ER -