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—Compressive sensing (CS) has been widely studied and applied in many fields. Recently, the way to perform secure compressive sensing (SCS) has become a topic of growing interest. The existing works on SCS usually take the sensing matrix as a key and the resultant security level is not evaluated in depth. They can only be considered as a preliminary(More)
—Recently, utilizing renewable energy for wireless system has attracted extensive attention. However, due to the instable energy supply and the limited battery capacity, renewable energy cannot guarantee to provide the perpetual operation for wireless sensor networks (WSN). The coexistence of renewable energy and electricity grid is expected as a promising(More)
Recently, a novel image cipher [Multimed Tools Appl (2012) 56:315–330] was proposed based on mixed transformed logistic maps. The cipher includes three parts: initial permutation of all the pixels with six odd keys, nonlinear diffusion using the first chaotic keystream and xoring the second chaotic keystream with the resultant values, and Zig-Zag diffusion(More)
—Recent research advances have revealed the computational secrecy of the compressed sensing (CS) paradigm. Perfect secrecy can also be achieved by normalizing the CS measurement vector. However, these findings are established on real measurements while digital devices can only store measurements at a finite precision. Based on the distribution of(More)
Since the first appearance in Fridrich's design, the usage of permutation-diffusion structure for designing digital image cryptosystem has been receiving increasing research attention in the field of chaos-based cryptography. Recently, a novel chaotic Image Cipher using one round Modified Permutation-Diffusion pattern (ICMPD) was proposed. Unlike(More)
Image feature encryption is comprised of feature extraction and feature encryption. The existing feature encryption algorithms aim at extracting edge features as significant information for encryption purpose rather than salient regions. However, salient regions in the images usually carry more important information than edge features. Moreover, most of(More)