Secure Heterogeneous Data Deduplication via Fog-Assisted Mobile Crowdsensing in 5G-Enabled IIoT

被引:19
|
作者
Zhang, Yuanyuan [1 ,2 ]
Chen, C. L. Philip [1 ,2 ]
机构
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
[2] Pazhou Lab, Guangzhou 510335, Peoples R China
基金
中国国家自然科学基金;
关键词
Sensors; Task analysis; Crowdsensing; Industrial Internet of Things; Data privacy; Data models; Servers; Fog-assisted; heterogeneous data; industrial Internet of Things (IIoT); mobile crowdsensing; secure deduplication;
D O I
10.1109/TII.2021.3099210
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mobile crowdsensing provides the data collection and sharing for the 5G-enabled industrial Internet of Things. However, the redundant and duplicated heterogeneous sensing data bring unnecessary heavy storage and communication overhead. In this article, we propose a secure heterogeneous data deduplication scheme, which introduces the privacy-preserving cosine similarity computing to eliminate the replicate sensing data without privacy leakage in mobile crowdsensing. Specifically, we use the proxy re-encryption algorithm to realize secure and accurate task assignment via fog-assisted mobile crowdsensing. Based on lightweight two-party random masking and polynomial aggregation techniques, we achieve the privacy-preserving cosine similarity computing protocol. Finally, we conduct the privacy analysis, and experimental results on real-world datasets show that our approach is practical and effective.
引用
收藏
页码:2849 / 2857
页数:9
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