Residential College | false |
Status | 已發表Published |
Embedding cryptographic features in compressive sensing | |
Yushu Zhang1,2,3; Jiantao Zhou2; Fei Chen3; Leo Yu Zhang2,3; Kwok-Wo Wong4; Xing He1; Di Xiao5 | |
2016-05-13 | |
Source Publication | Neurocomputing |
ISSN | 0925-2312 |
Volume | 205Pages:472-480 |
Abstract | 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 can only be considered as preliminary explorations on SCS. In this paper, we firstly propose some possible encryption models for CS. It is believed that these models will provide a new point of view and stimulate further research in both CS and cryptography. Then, we demonstrate that random permutation is an acceptable permutation with overwhelming probability, which can effectively relax the Restricted Isometry Constant for parallel compressive sensing. Moreover, random permutation is utilized to design a secure parallel compressive sensing scheme. Security analysis indicates that the proposed scheme can achieve the asymptotic spherical secrecy. Meanwhile, the realization of chaos is used to validate the feasibility of one of the proposed encryption models for CS. Lastly, results verify that the embedding random permutation based encryption enhances the compression performance and the scheme possesses high transmission robustness against additive white Gaussian noise and cropping attack. |
Keyword | Parallel Compressive Sensing Random Permutation Secure Compressive Sensing Symmetric-key Cipher |
DOI | 10.1016/j.neucom.2016.04.053 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Computer Science |
WOS Subject | Computer Science, Artificial Intelligence |
WOS ID | WOS:000378952500044 |
The Source to Article | Scopus |
Scopus ID | 2-s2.0-84969579608 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE Faculty of Science and Technology |
Affiliation | 1.Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, SouthwestUniversity, Chongqing 400715, China. 2.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 3.College of Computer Science and Engineering, Shenzhen University, Shenzhen 518060, China 4.Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong 5.College of Computer Science, Chongqing University, Chongqing 400044, China |
First Author Affilication | Faculty of Science and Technology |
Recommended Citation GB/T 7714 | Yushu Zhang,Jiantao Zhou,Fei Chen,et al. Embedding cryptographic features in compressive sensing[J]. Neurocomputing, 2016, 205, 472-480. |
APA | Yushu Zhang., Jiantao Zhou., Fei Chen., Leo Yu Zhang., Kwok-Wo Wong., Xing He., & Di Xiao (2016). Embedding cryptographic features in compressive sensing. Neurocomputing, 205, 472-480. |
MLA | Yushu Zhang,et al."Embedding cryptographic features in compressive sensing".Neurocomputing 205(2016):472-480. |
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Embedding cryptograp(1505KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download |
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