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Privacy-preserving task allocation for edge computing-based mobile crowdsensing
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PRE
AI
DOI：10.1016/j.compeleceng.2021.107528.png)
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摘要
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In the era of big data, edge computing has coped greatly with the increase in data. Recently, edge computing has been incorporated into mobile crowdsensing (MCS) to collect large-scale data, but existing edge computing-based MCS (EC-MCS) ideally assumes that edge servers are trusted. In this paper, a novel mechanism is proposed that we use semi-honest entities to securely and efficiently complete task assignment in large-scale crowdsensing. Firstly, homomorphic encryption is used to encrypt users' location information, and the collaboration between edge servers is used to complete task allocation under cipher-text. Then, the optimal users are selected to complete tasks and upload the encrypted sensing data. Moreover, a secure payment mechanism is proposed to avoid fraud problems in semi-honest edge servers. Finally, we analyze the security of our scheme theoretically and conduct a multi-dimensional simulation experiment to prove the effectiveness and availability of the proposed scheme.
Keyword:
Big data
Crowdsensing
Edge computing
Task allocation
Homomorphic encryption
Privacy preservation
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
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