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Towards truthful auction for big data trading
An, Dou1; Yang, Qingyu2; Yu, Wei3; Li, Donghe4; Zhang, Yang4; Zhao, Wei5
2018-02-02
Conference Name36th IEEE International Performance Computing and Communications Conference, IPCCC 2017
Source Publication2017 IEEE 36th International Performance Computing and Communications Conference, IPCCC 2017
Volume2018-January
Pages1-7
Conference Date1210, 2017 - 1212, 2017
Conference PlaceSan Diego, CA, United states
Author of SourceInstitute of Electrical and Electronics Engineers Inc.
AbstractIn this paper, we address the issue of data trading in big data markets. Data trading problems have attracted increased attention recently, as the economic benefits and potential of big data trading are substantial and varied. However, how to effectively trade data between the data owners (sellers) and data collectors/users (buyers) is far from settled, and requires careful design. Auction mechanisms have been applied across many fields, and have significant potential to facilitate data transactions in a fair, truthful, and secure way. Nonetheless, a truthful auction must ensure the property of incentive compatibility, meaning that the bidders can obtain highest utility if and only if they submit their bids and asks truthfully. Furthermore, a truthful and fair auction should also protect the optimal auction results from being manipulated by false-name bidding attacks, where users (participants) utilize multiple identities or accounts to influence the auction results. To tackle these issues, we propose a Multi-round False-name Proof Auction (MFPA) scheme, which enables data trading among data owners (sellers) and data collectors (buyers). We prove that our MFPA scheme achieves the properties of incentive compatibility, false-name bidding proofness, and computational efficiency. The experimental results demonstrate that MFPA achieves good performance in terms of social surplus, satisfaction ratio, and computation overhead. © 2017 IEEE.
DOI10.1109/PCCC.2017.8280501
Language英语
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.MOE Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Shaanxi, China;
2.SKLMSE Lab, School of Electronic and Information Engineering, Xian Jiaotong University, Shaanxi, China;
3.Department of Computer and Information Sciences, Towson University, MD, United States;
4.School of Electronic and Information Engineering, Xi'an Jiaotong University, Shaanxi, China;
5.University of Macau, China
Recommended Citation
GB/T 7714
An, Dou,Yang, Qingyu,Yu, Wei,et al. Towards truthful auction for big data trading[C]//Institute of Electrical and Electronics Engineers Inc.,2018:1-7.
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