A Multi-level Intelligent Selective Encryption Control Model for Multimedia Big Data Security in Sensing System with Resource Constraints

Chen Xiao, Lifeng Wang, Zhu Jie, Tiemeng Chen
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引用次数: 10

Abstract

The multimedia big data in multimedia sensing and other IoT (Internet of Things) systems are high-volume, real-time, dynamic and heterogeneous. These characteristics lead to new challenges of data security. When computation and power resources in some IoT nodes are very scarce, these challenges become more serious that complex data security process on multimedia data is restricted by the aforementioned limited resources. Hence, the confidentiality of multimedia big data under resources constraints is investigated in this paper. Firstly, the growth trend of data volume compared with computational resources is discussed, and an analysis model for multimedia data encryption optimization is proposed. Secondly, a general-purpose lightweight speed tunable video encryption scheme is introduced. Thirdly, a series of intelligent selective encryption control models are proposed. Fourthly, the performance of proposed schemes is evaluated by experimental analyses and proves that schemes are effective enough to support real-time encryption of multimedia big data. Additionally, in the age of big data and cloud computing, the aforementioned analysis method can also be applied to other systems with limited resources.
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具有资源约束的传感系统多媒体大数据安全多级智能选择加密控制模型
多媒体传感等物联网系统中的多媒体大数据具有大容量、实时性、动态性和异构性。这些特点给数据安全带来了新的挑战。当一些物联网节点的计算和电力资源非常稀缺时,多媒体数据的复杂数据安全处理受到上述有限资源的制约,这些挑战变得更加严重。因此,本文研究了资源约束下多媒体大数据的保密性问题。首先,讨论了数据量相对于计算资源的增长趋势,提出了多媒体数据加密优化的分析模型。其次,介绍了一种通用的轻量级速度可调视频加密方案。第三,提出了一系列智能选择性加密控制模型。第四,通过实验分析对所提方案的性能进行了评价,证明了所提方案的有效性足以支持多媒体大数据的实时加密。此外,在大数据和云计算时代,上述分析方法也可以应用于其他资源有限的系统。
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