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International Conference on Big Data Computing and Communications

BigCom 2016: Big Data Computing and Communications pp 13–26Cite as

Privacy-Preserving Strategyproof Auction Mechanisms for Resource Allocation in Wireless Communications

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9784))

Abstract

In recent years, auction theory has been extensively studied and many state-of-art solutions have been proposed aiming at allocating scarce resources (e.g. spectrum resources in wireless communications). Unfortunately, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. On the other hand, performance guarantee, such as social efficiency maximization, is also crucial for auction mechanism design. Therefore, the goal of this work is to design a series of strategyproof and privacy preserving auction mechanisms that maximize the social efficiency. To make the designed auction model more general, we allow the bidders to express their preferences about multiple items, which is often regarded as the multi-unit auction. As computing an optimal allocation in multi-unit auction is NP-hard, we design a set of near optimal allocation mechanisms with privacy preserving separately for: (1) The auction aims at identical multi-items trading; and (2) The auction aims at distinct multi-items trading, which is also known as combinatorial auction. To the best of our knowledge, we are the first to design strategyproof multi-unit auction mechanisms with privacy preserving, which maximize the social efficiency at the same time. The evaluation results corroborate our theoretical analysis, and show that our proposed methods achieve low computation and communication complexity.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (NSFC) under Grant No. 61572342, No. 61303206, Natural Science Foundation of Jiangsu Province under Grant No. BK20151240, China Postdoctoral Science Foundation under Grant No. 2015M580470. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of author(s) and do not necessarily reflect the views of the funding agencies (NSFC).

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Authors and Affiliations

  1. School of Urban Rail Transportation, Soochow University, Suzhou, China

    Yu-E Sun

  2. School of Computer Science and Technology, Soochow University, Suzhou, China

    He Huang

  3. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Yu-E Sun, He Huang, Xiang-Yang Li, Yang Du, Miaomiao Tian, Hongli Xu & Mingjun Xiao

Authors
  1. Yu-E Sun
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  2. He Huang
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  3. Xiang-Yang Li
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  4. Yang Du
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  5. Miaomiao Tian
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  6. Hongli Xu
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  7. Mingjun Xiao
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Corresponding author

Correspondence to He Huang .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of N. Carolina at Charlotte, Charlotte, North Carolina, USA

    Yu Wang

  2. Northeastern University, College of Information Science and Engineering, Shenyang, Liaoning, China

    Ge Yu

  3. Department of Electrical & Computer Engineering, Rutgers University, Piscataway, New Jersey, USA

    Yanyong Zhang

  4. Department of Electrical and Computer Engineering, University of Houston Department of Engineering, Houston, Texas, USA

    Zhu Han

  5. College of Information Science and Engineering, Northeastern University, Shenyang , Liaoning, China

    Guoren Wang

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© 2016 Springer International Publishing Switzerland

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Sun, YE. et al. (2016). Privacy-Preserving Strategyproof Auction Mechanisms for Resource Allocation in Wireless Communications. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-42553-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42552-8

  • Online ISBN: 978-3-319-42553-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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