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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 PublicationNeurocomputing
ISSN0925-2312
Volume205Pages: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. 

KeywordParallel Compressive Sensing Random Permutation Secure Compressive Sensing Symmetric-key Cipher
DOI10.1016/j.neucom.2016.04.053
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000378952500044
The Source to ArticleScopus
Scopus ID2-s2.0-84969579608
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Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Faculty of Science and Technology
Affiliation1.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 AffilicationFaculty 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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