TY - GEN
T1 - Sybil-Proof Online Incentive Mechanisms for Crowdsensing
AU - Lin, Jian
AU - Li, Ming
AU - Yang, Dejun
AU - Xue, Guoliang
N1 - Funding Information:
J. Lin, M. Li are with Colorado School of Mines, Golden, CO 80401 USA. D. Yang (corresponding author) is with Colorado School of Mines, Golden, CO 80401 USA and Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023 China (e-mail: {jilin, mili, djyang}@mines.edu). G. Xue is with Arizona State University, Tempe, AZ 85287 USA (e-mail: xue@asu.edu). This research was supported in part by NSF grants 1421685, 1444059, 1717197, 1717315, and Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, NJUPT. The information reported here does not reflect the position or the policy of the federal government.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - Crowdsensing leverages the rapid growth of sensor-embedded smartphones and human mobility for pervasive information collection. To incentivize smartphone users to participate in crowdsensing, many auction-based incentive mechanisms have been proposed for both offline and online scenarios. It has been demonstrated that the Sybil attack may undermine these mechanisms. In a Sybil attack, a user illegitimately pretends multiple identities to gain benefits. Sybil-proof incentive mechanisms have been proposed for the offline scenario. However, the problem of designing Sybil-proof online incentive mechanisms for crowdsensing is still open. Compared to the offline scenario, the online scenario provides users one more dimension of flexibility, i.e., active time, to conduct Sybil attacks, which makes this problem more challenging. In this paper, we design Sybil-proof online incentive mechanisms to deter the Sybil attack for crowdsensing. Depending on users' flexibility on performing their tasks, we investigate both single-minded and multi-minded cases and propose SOS and SOM, respectively. SOS achieves computational efficiency, individual rationality, truthfulness, and Sybil-proofness. SOM achieves individual rationality, truthfulness, and Sybil-proofness. Through extensive simulations, we evaluate the performance of SOS and SOM.
AB - Crowdsensing leverages the rapid growth of sensor-embedded smartphones and human mobility for pervasive information collection. To incentivize smartphone users to participate in crowdsensing, many auction-based incentive mechanisms have been proposed for both offline and online scenarios. It has been demonstrated that the Sybil attack may undermine these mechanisms. In a Sybil attack, a user illegitimately pretends multiple identities to gain benefits. Sybil-proof incentive mechanisms have been proposed for the offline scenario. However, the problem of designing Sybil-proof online incentive mechanisms for crowdsensing is still open. Compared to the offline scenario, the online scenario provides users one more dimension of flexibility, i.e., active time, to conduct Sybil attacks, which makes this problem more challenging. In this paper, we design Sybil-proof online incentive mechanisms to deter the Sybil attack for crowdsensing. Depending on users' flexibility on performing their tasks, we investigate both single-minded and multi-minded cases and propose SOS and SOM, respectively. SOS achieves computational efficiency, individual rationality, truthfulness, and Sybil-proofness. SOM achieves individual rationality, truthfulness, and Sybil-proofness. Through extensive simulations, we evaluate the performance of SOS and SOM.
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U2 - 10.1109/INFOCOM.2018.8486418
DO - 10.1109/INFOCOM.2018.8486418
M3 - Conference contribution
AN - SCOPUS:85056153580
T3 - Proceedings - IEEE INFOCOM
SP - 2438
EP - 2446
BT - INFOCOM 2018 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Conference on Computer Communications, INFOCOM 2018
Y2 - 15 April 2018 through 19 April 2018
ER -