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Budget-Feasible Sybil-Proof Mechanisms for Crowdsensing

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Frontiers of Algorithmic Wisdom (IJTCS-FAW 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13461))

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Abstract

The rapid use of smartphones and devices leads to the development of crowdsensing (CS) systems where a large crowd of participants can take part in performing data collecting tasks in large-scale distributed networks. Participants/users in such systems are usually selfish and have private information, such as costs and identities. Budget-feasible mechanism design, as a sub-field of auction theory, is a useful paradigm for crowdsensing, which naturally formulates the procurement scenario with buyers’ budgets being considered and allows the users to bid their private costs. Although the bidding behavior is well-regulated, budget-feasible mechanisms are still vulnerable to the Sybil attack where users may generate multiple fake identities to manipulate the system. Thus, it is vital to provide Sybil-proof budget-feasible mechanisms for crowdsensing. In this paper, we design a budget-feasible incentive mechanism which can guarantee truthfulness and deter Sybil attack. We prove that the proposed mechanism achieves individual rationality, truthfulness, budget feasibility, and Sybil-proofness. Extensive simulation results further validate the efficiency of the proposed mechanism.

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Acknowledgements

The work is supported in part by the National Key Research and Development Program of China under grant No. 2019YFB2102200, National Natural Science Foundation of China under Grant No. 61672154, 61672370, 61972086 and the Postgraduate Research & Practice Innovation Program of Jiangsu Province under grant No. KYCX19_0089.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Xiang Liu, Weiwei Wu, Wanyuan Wang & Yuhang Xu

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Xiumin Wang

  3. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China

    Helei Cui

Authors
  1. Xiang Liu
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  2. Weiwei Wu
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  3. Wanyuan Wang
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  4. Yuhang Xu
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  5. Xiumin Wang
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  6. Helei Cui
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Corresponding author

Correspondence to Weiwei Wu .

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Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Minming Li

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xiaoming Sun

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Liu, X., Wu, W., Wang, W., Xu, Y., Wang, X., Cui, H. (2022). Budget-Feasible Sybil-Proof Mechanisms for Crowdsensing. In: Li, M., Sun, X. (eds) Frontiers of Algorithmic Wisdom. IJTCS-FAW 2022. Lecture Notes in Computer Science, vol 13461. Springer, Cham. https://doi.org/10.1007/978-3-031-20796-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-20796-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20795-2

  • Online ISBN: 978-3-031-20796-9

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