Xiao Di, Li Min, Zheng Hongying
College of Computer Science, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2020 Mar 10;20(5):1517. doi: 10.3390/s20051517.
Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure.
近年来，物联网（IoT）的快速发展导致了非标量数据（如图像、视频）呈指数级增长。本地服务远远无法满足存储需求，而云计算也无法有效支持异构分布式物联网环境，如无线传感器网络。为了有效地为视频数据存储提供智能隐私保护，我们充分利用边缘计算的三种模式（多接入边缘计算、微云及雾计算）来设计分层边缘计算架构，并在此基础上提出了一种低复杂度、高安全性的方案。视频被分为三个部分并存储在完全不同的设施中。具体而言，关键帧的最高有效位直接存储在本地传感器设备中，而关键帧的最低有效位则被加密并发送到半可信微云。非关键帧通过两层并行压缩感知进行压缩，并由二维逻辑斜帐篷映射加密，然后传输到云端。仿真实验和理论分析表明，我们提出的方案不仅可以基于分层边缘计算为大型视频数据存储提供智能隐私保护，还能避免增加额外的计算负担和存储压力。
