Secure Communications With Asymptotically Gaussian Compressed Encryption

Cho, Wonwoo; Yu, Nam Yul

doi:10.1109/lsp.2017.2773128

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…Cho and Yu studied a particular measurement matrix for secure communication in the multicarrier system [75]. The measurement matrix is given as…”

Section: A Wireless Wiretap Channelmentioning

confidence: 99%

“…CS-Based Secrecy [68] Build up a secure wireless wiretap channel by leveraging the channel asymmetry Secrecy Capacity [69] Quantitatively investigate the lower and upper bounds of the secrecy capacity The Secrecy Based on Distributed CS [126] Design an amplify-forward scheme to cater the distribute nature of wireless sensor network The Secrecy Based on MIMO Precoding [71] Simultaneously maximize the secrecy and the signal-to-noise ratio The Secrecy Based on Circulant Matrix [72] Guarantee the wireless indistinguishability security with some conditions Multicarrier System [73]- [75] Induce artificial noise/channel randomization/particular measurement matrix for the security Cooperative Networks [76] Own the superiority of energy harvesting and high secrecy capacity Establishing Secure Measurement Matrix [77], [78] Design measurement matrix with reciprocal quantization/channel measurements Integrity-Protected CS [79] AES for the encryption of measurements and hash algorithm for integrity checking Capturing Medical Data [80] Capture data firstly and then encrypt them Data Gathering [81]- [84] Combine pseudorandom permutations and symmetric/additively homomorphic encryption Compressed Detection [85], [86] Perform collaborative compressed detection at distributed nodes Adaptive CS for Smart Objects [87] Utilize the information of smart objects to adapt the CS measurement condition Frequency Selection for Static Environment [88], [89] Enlarge the entropy of measured channel and accelerate the rate of generating keys Chaotic CS for Internet of Multimedia Things [90] Realize low-cost sampling and confidentiality preservation Secure Interaction with Cloud [91] Random compressed encryption for the raw data Crowdsensing [92]- [94] Maximize the geographic map coverage and protect the participants' trace privacy Smart Grid [95]- [97] Construct a secret measurement matrix for joint encryption, sampling, and compression Wireless Body Area Networks [98]- [100] Exploit chaotic CS for energy saving and data security a long delay off over fading channels…”

Section: Security Model Performancementioning

confidence: 99%

“…Yu considered the circulant matrices for wireless security and demonstrated the indistinguishability depending on the measure of relative entropy [72]. In addition, many works based on CS were proposed for different wireless communication security scenarios such as multicarrier system [73]- [75], cooperative networks [76], wireless sensor network (WSN) [77]- [86], internet of things (IoT) [87]- [91], crowdsensing [92]- [94], smart grid [95]- [97], and wireless body area networks [98]- [100].…”

Section: Introductionmentioning

confidence: 99%

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Secure Wireless Communications Based on Compressive Sensing: A Survey

Zhang

Xiang

Zhang

et al. 2019

IEEE Commun. Surv. Tutorials

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Compressive sensing (CS) has become a popular signal processing technique and has extensive applications in numerous fields such as wireless communications, image processing, magnetic resonance imaging, remote sensing imaging, and anology to information conversion, since it can realize simultaneous sampling and compression. In the information security field, secure CS has received much attention due to the fact that CS can be regarded as a cryptosystem to attain simultaneous sampling, compression and encryption when maintaining the secret measurement matrix. Considering that there are increasing works focusing on secure wireless communications based on CS in recent years, we produce a detailed review for the state-ofthe-art in this paper. To be specific, the survey proceeds with two phases. The first phase reviews the security aspects of CS according to different types of random measurement matrices such as Gaussian matrix, circulant matrix, and other special random matrices, which establishes theoretical foundations for applications in secure wireless communications. The second phase reviews the applications of secure CS depending on communication scenarios such as wireless wiretap channel, wireless sensor network, internet of things, crowdsensing, smart grid, and wireless body area networks. Finally, some concluding remarks are given.

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“…Cho and Yu studied a particular measurement matrix for secure communication in the multicarrier system [75]. The measurement matrix is given as…”

Section: A Wireless Wiretap Channelmentioning

confidence: 99%

Section: Security Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Secure Wireless Communications Based on Compressive Sensing: A Survey

Zhang

Xiang

Zhang

et al. 2019

IEEE Commun. Surv. Tutorials

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“…Then, they quantitatively showed that the B-OTS cryptosystem and its class dependent variations can be resistant against known plaintext attacks in [37]. In [38], the security of the asymptotically Gaussian-OTS (AG-OTS) cryptosystem, which employs random Bernoulli matrices multiplied by a unitary matrix, has been discussed in the presence of wireless channels. In addition, the indistinguishability [39] of the G-OTS and the B-OTS cryptosystems has been studied in [40], which turned out to be highly sensitive to energy variation of plaintexts.…”

Section: Introductionmentioning

confidence: 99%

Secure and Efficient Compressed Sensing-Based Encryption With Sparse Matrices

Cho

2020

IEEE Trans.Inform.Forensic Secur.

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In this paper, we study the security of a compressed sensing (CS) based cryptosystem called a sparse one-time sensing (S-OTS) cryptosystem, which encrypts a plaintext with a sparse measurement matrix. To construct the secret matrix and renew it at each encryption, a bipolar keystream and a random permutation pattern are employed as cryptographic primitives, which can be obtained by a keystream generator of stream ciphers. With a small number of nonzero elements in the measurement matrix, the S-OTS cryptosystem achieves efficient CS encryption in terms of memory and computational cost. In security analysis, we show that the S-OTS cryptosystem can be indistinguishable as long as each plaintext has constant energy, which formalizes computational security against ciphertext only attacks (COA). In addition, we consider a chosen plaintext attack (CPA) against the S-OTS cryptosystem, which consists of two sequential stages, keystream and key recovery attacks. Against keystream recovery under CPA, we demonstrate that the S-OTS cryptosystem can be secure with overwhelmingly high probability, as an adversary needs to distinguish a prohibitively large number of candidate keystreams. Finally, we conduct an information-theoretic analysis to show that the S-OTS cryptosystem can be resistant against key recovery under CPA by guaranteeing that the probability of success is extremely low. In conclusion, the S-OTS cryptosystem can be computationally secure against COA and the two-stage CPA, while providing efficiency in CS encryption.

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