Location and Bid Privacy Preserving-Based Quality-Aware Worker Recruitment Scheme in MCS

Mobile crowd sensing (MCS) has become a prevalent large-scale and low-cost data collection paradigm by employing workers, and the location and bid privacy of both task and workers should not be leaked to the third party to prevent the adversary from attacking. Existing privacy preserving worker recr...

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Vydáno v:IEEE internet of things journal Ročník 11; číslo 12; s. 21841 - 21856
Hlavní autoři: Shi, Weifan, Deng, Qingyong, Li, Zhetao, Long, Saiqin, Liu, Haolin, Pang, Xiaoyi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Cryptography
Data collection
Data privacy
Encryption
Greedy algorithms
Location and bid privacy
mobile crowd sensing (MCS)
Privacy
privacy preserving
quality aware
Recruitment
Sensors
Task analysis
worker recruitment
Workers
ISSN:2327-4662, 2327-4662
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Popis
Shrnutí:Mobile crowd sensing (MCS) has become a prevalent large-scale and low-cost data collection paradigm by employing workers, and the location and bid privacy of both task and workers should not be leaked to the third party to prevent the adversary from attacking. Existing privacy preserving worker recruitment schemes have taken the location and quality into consideration, but ignore the bid privacy. To tackle this issue, a two-stage location and bid privacy preserving-based quality-aware worker recruitment (LBPP-QWR) scheme is proposed in this article. In the first stage, to select those workers who satisfy the specified location and bid range of the task in the encrypted state, we propose a hybrid encryption scheme of matrix encryption and asymmetric encryption technique in the MCS platform. For the second stage, after obtaining the preliminary worker set via the platform, we propose a knapsack worker selection (KWS) algorithm to recruit those high-quality and low-bid workers under the budget constraint in the data requester (DR). Considering that there are quality-unknown workers, we further propose an improved <inline-formula> <tex-math notation="LaTeX">\epsilon </tex-math></inline-formula>-KWS algorithm based on <inline-formula> <tex-math notation="LaTeX">\epsilon </tex-math></inline-formula>-greedy algorithm by combining the exploration and exploitation mechanism to learn the quality of worker. Extensive experiments conducted on real-world data sets demonstrate that our proposed scheme can improve the average total quality by 17.96%-83.34%, and the cost efficiency by 27.99%-67.90% for the DR compared with other benchmark methods.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3376799