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author:

Luo, Bin (Luo, Bin.) [1] | Li, Xinghua (Li, Xinghua.) [2] | Liu, Ximeng (Liu, Ximeng.) [3] | Guo, Jingjing (Guo, Jingjing.) [4] | Ren, Yanbing (Ren, Yanbing.) [5] | Ma, Siqi (Ma, Siqi.) [6] | Ma, Jianfeng (Ma, Jianfeng.) [7]

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EI

Abstract:

When enjoying mobile crowdsensing (MCS), it is vital to evaluate the trustworthiness of mobile users (MUs) without disclosing their sensitive information. However, the existing schemes ignore this requirement in the multiple crowdsourcers (CSs) scenario. The lack of a credible sharing about MUs' trustworthiness results in an inaccurate trust evaluation, disabling allocating tasks to reliable MUs. To address it, based on the analysis of the desired properties, we propose a scheme enabling dependable data collection with multiple crowdsourcers trust sharing (D2MTS). Specifically, we design the MU anonymous management. Two kinds of MU generated pseudonym systems without relationships are presented to mark each MU in trust evaluation and task execution, respectively. Through the devised pseudonym changes on these pseudonyms and the common token distribution algorithm, D2MTS realizes privacy-preserving trust sharing. Moreover, to guarantee credible sharing, based on the hash chain, D2MTS records MUs' trustworthiness with the unforgeable signature on the blockchain established by multiple CSs which do not trust each other naturally. Extensive experiments show that compared with the other works, D2MTS's detection ratio of vicious MUs and the percentage of reliable MUs among the selected ones can increase by 208.61% and 28.27%. Both computational and communication delays are limited. © 1989-2012 IEEE.

Keyword:

Blockchain Chains Data acquisition Data privacy Job analysis Reliability analysis

Community:

  • [ 1 ] [Luo, Bin]Xidian University, State Key Laboratory of Integrated Services Networks, The School of Cyber Engineering, Xi'an; 710071, China
  • [ 2 ] [Li, Xinghua]Xidian University, State Key Laboratory of Integrated Services Networks, The School of Cyber Engineering, Xi'an; 710071, China
  • [ 3 ] [Liu, Ximeng]Fuzhou University, College of Mathematics and Computer Science, Fujian; 350108, China
  • [ 4 ] [Guo, Jingjing]Xidian University, State Key Laboratory of Integrated Services Networks, The School of Cyber Engineering, Xi'an; 710071, China
  • [ 5 ] [Ren, Yanbing]Xidian University, State Key Laboratory of Integrated Services Networks, The School of Cyber Engineering, Xi'an; 710071, China
  • [ 6 ] [Ma, Siqi]University of New South Wales, School of Engineering and Information Technology, Sydney; NSW; 2052, Australia
  • [ 7 ] [Ma, Jianfeng]Xidian University, State Key Laboratory of Integrated Services Networks, The School of Cyber Engineering, Xi'an; 710071, China

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Source :

IEEE Transactions on Knowledge and Data Engineering

ISSN: 1041-4347

Year: 2024

Issue: 3

Volume: 36

Page: 927-942

8 . 9 0 0

JCR@2023

CAS Journal Grade:1

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SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 2

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