Embedding cryptographic features in compressive sensing
Zhang Y.1,2,3; Zhou J.2; Chen F.3; Zhang L.Y.2,3; Wong K.-W.4; He X.1; Xiao D.5
Source PublicationNeurocomputing

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. © 2016 Elsevier B.V.

KeywordParallel Compressive Sensing Random Permutation Secure Compressive Sensing Symmetric-key Cipher
URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000378952500044
The Source to ArticleScopus
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Cited Times [WOS]:68   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorZhang Y.
Affiliation1.Southwest Univ, Chongqing Key Lab Nonlinear Circuits & Intelligen, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China
2.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau, Peoples R China
3.Shenzhen Univ, Coll Comp Sci & Engn, Shenzhen 518060, Peoples R China
4.City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China
5.Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhang Y.,Zhou J.,Chen F.,et al. Embedding cryptographic features in compressive sensing[J]. Neurocomputing,2016,205:472.
APA Zhang Y..,Zhou J..,Chen F..,Zhang L.Y..,Wong K.-W..,...&Xiao D..(2016).Embedding cryptographic features in compressive sensing.Neurocomputing,205,472.
MLA Zhang Y.,et al."Embedding cryptographic features in compressive sensing".Neurocomputing 205(2016):472.
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