TY - GEN
T1 - Sybil-proof incentive mechanisms for crowdsensing
AU - Lin, Jian
AU - Li, Ming
AU - Yang, Dejun
AU - Xue, Guoliang
AU - Tang, Jian
N1 - Funding Information:
This research was supported in part by NSF grants 1421685, 1444059, 1461886 and 1525920. The information reported here does not reflect the position or the policy of the federal government. J. Lin, M. Li and D. Yang are with Colorado School of Mines, Golden, CO 80401 USA (e-mail: {jilin, mili, djyang}@mines.edu). G. Xue is with Arizona State University, Tempe, AZ 85287 USA (e-mail: xue@asu.edu). J. Tang is with Syracuse University, NY 13210 (e-mail: jtang02@syr.edu).
Publisher Copyright:
© 2017 IEEE.
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.
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U2 - 10.1109/INFOCOM.2017.8057175
DO - 10.1109/INFOCOM.2017.8057175
M3 - Conference contribution
AN - SCOPUS:85034080489
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2017 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Conference on Computer Communications, INFOCOM 2017
Y2 - 1 May 2017 through 4 May 2017
ER -