A grid and consensus based privacy preservation scheme for crowdsensing-Reference-Cited by-同舟云学术

A grid and consensus based privacy preservation scheme for crowdsensing

Published:2023-07-25 Issue: Volume: Page:
ISSN:
Container-title:
language:
Short-container-title:

Author:

Zheng Xiao dong¹^ORCID,Cui Lianhe²,Yuan Qi²,Feng Guangsheng¹

Affiliation:

1. Harbin Engineering University

2. Qiqihar University

Abstract

AbstractIn the process of task allocation in crowdsensing, the precise location of the sensing initiator or the location of the sensing user may be released to each other, which will violate the privacy of various users in crowdsensing. Thus, in order to cope with the problem of privacy leakage in crowdsensing, and further deal with the problem of inconformity in feeding back results, in this paper, a privacy preservation scheme for crowdsensing which is based on the conception of division grids and distributed consensus (short for GDDCC) has been proposed. In this scheme, the region used to publish sensing tasks is divided into smaller girds with enough sensing users, and a sub-area with sensing grids is sent to all users in this region. Then sensing users in each grid unify their sensing results and send the consensus result as the precise result to the sensing initiator. As a result, during the process of crowdsensing, the sensing initiator does not know the precise location of the sensing user and the sensing user does not know the precise location of the sensing initiator, so the privacy of each entity in the crowdsensing is preserved. In addition, the idea of prefix membership verification is used to preserve the privacy of sensing users in the same grid during the process of result consensus. Then in the section of security analysis, the privacy security of each entity is analysed, and in the section of performance analysis, experimental results with elaborate reasons are given to further demonstrate the superiority of the proposed GDDCC.

Publisher

Research Square Platform LLC

Reference46 articles.

1. Sadhu, V., Zonouz, S., Sritapan, V., & Pompili, D. (2021). “CollabLoc: Privacy-Preserving Multi-Modal Collaborative Mobile Phone Localization,” Ieee Transactions on Mobile Computing, vol. 20, no. 1, pp. 104–116, Jan,

2. Huang, P., Zhang, X. N., Guo, L. K., & Li, M. (2021). “Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations,” Ieee Transactions on Mobile Computing, vol. 20, no. 2, pp. 519–532, Feb,

3. Zou, S. H., Xi, J. W., Wang, H. G., & Xu, G. A. (2020). “CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System,” Ieee Transactions on Industrial Informatics, vol. 16, no. 6, pp. 4206–4218, Jun,

4. Zhang, Y. H., Li, M., Yang, D. J., Tang, J., Xue, G. L., & Xu, J. (2020). “Tradeoff Between Location Quality and Privacy in Crowdsensing: An Optimization Perspective,” Ieee Internet of Things Journal, vol. 7, no. 4, pp. 3535–3544, Apr,

5. Zhang, L., Liu, D., Chen, M., Li, H., Wang, C., Zhang, Y., & Du, Y., “A user collaboration privacy protection scheme with threshold scheme and smart contract,”Information Sciences, vol. 560, pp.183–201, 2021/06/01/, 2021.