Secure Heterogeneous Data Deduplication via Fog-Assisted Mobile Crowdsensing in 5G-Enabled IIoT
Yuanyuan Zhang, C. L. Philip Chen
Abstract
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.