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.… Click to show full 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.
               
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