On the Security of Compressed Sensing-Based Signal Cryptosystem

With the development of the cyber-physical systems (CPS), the security analysis of the data therein becomes more and more important. Recently, due to the advantage of joint encryption and compression for data transmission in CPS, the emerging compressed sensing (CS)-based cryptosystem has attracted much attention, where security is of extreme importance. The existing methods only analyze the security of the plaintext under the assumption that the key is absolutely safe. However, for sparse plaintext, the prior sparsity knowledge of the plaintext could be exploited to partly retrieve the key, and then the plaintext, from the ciphertext. So, the existing methods do not provide a satisfactory security analysis. In this paper, it is conducted in the information theory frame, where the plaintext sparsity feature and the mutual information of the ciphertext, key, and plaintext are involved. In addition, the perfect secrecy criteria (Shannon-sense and Wyner-sense) are extended to measure the security. While the security level is given, the illegal access risk is also discussed. It is shown that the CS-based cryptosystem achieves the extended Wyner-sense perfect secrecy, but when the key is used repeatedly, both the plaintext and the key could be conditionally accessed.

[1]  Andries P. Hekstra,et al.  Dependence balance bounds for single-output two-way channels , 1989, IEEE Trans. Inf. Theory.

[2]  Xinpeng Zhang,et al.  Lossy Compression and Iterative Reconstruction for Encrypted Image , 2011, IEEE Transactions on Information Forensics and Security.

[3]  Yong Xiang,et al.  Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Fabian J. Theis,et al.  Sparse component analysis and blind source separation of underdetermined mixtures , 2005, IEEE Transactions on Neural Networks.

[5]  Matthieu R. Bloch,et al.  Wireless Information-Theoretic Security , 2008, IEEE Transactions on Information Theory.

[6]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[7]  T. Blumensath,et al.  Theory and Applications , 2011 .

[8]  Vinod M. Prabhakaran,et al.  On compressing encrypted data , 2004, IEEE Transactions on Signal Processing.

[9]  Yunhao Liu,et al.  Special Issue on Cyber-Physical Systems (CPS)—Part II , 2013 .

[10]  Kyuwan Choi,et al.  Detecting the Number of Clusters in n-Way Probabilistic Clustering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Neri Merhav,et al.  Perfectly Secure Encryption of Individual Sequences , 2011, IEEE Transactions on Information Theory.

[12]  R. Gray Entropy and Information Theory , 1990, Springer New York.

[13]  A. D. Wyner,et al.  The wire-tap channel , 1975, The Bell System Technical Journal.

[14]  Zhaoshui He,et al.  Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  G. Sharma,et al.  On the security and robustness of encryption via compressed sensing , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[16]  Hugo Krawczyk,et al.  On Compression of Data Encrypted With Block Ciphers , 2012, IEEE Transactions on Information Theory.

[17]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[18]  Shengli Xie,et al.  Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization , 2011, IEEE Transactions on Image Processing.

[19]  Shengli Xie,et al.  Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources , 2011, IEEE Transactions on Neural Networks.

[20]  Y. Rachlin,et al.  The secrecy of compressed sensing measurements , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[21]  Lie Wang,et al.  New Bounds for Restricted Isometry Constants , 2009, IEEE Transactions on Information Theory.

[22]  Yaakov Tsaig,et al.  Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.

[23]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[24]  Gitta Kutyniok,et al.  1 . 2 Sparsity : A Reasonable Assumption ? , 2012 .

[25]  Yeow Meng Chee,et al.  On the Security of Index Coding With Side Information , 2011, IEEE Transactions on Information Theory.

[26]  Koji Nuida,et al.  On the Security of Pseudorandomized Information-Theoretically Secure Schemes , 2013, IEEE Transactions on Information Theory.

[27]  Liang Chen,et al.  Scrambling-based speech encryption via compressed sensing , 2012, EURASIP J. Adv. Signal Process..

[28]  Wei Wang,et al.  Compressive sensing-based data encryption system with application to sense-through-wall UWB noise radar , 2016, Secur. Commun. Networks.

[29]  Yong Xiang,et al.  Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Babak Seyfe,et al.  Perfect secrecy via compressed sensing , 2010, 2013 Iran Workshop on Communication and Information Theory.

[31]  Yonina C. Eldar,et al.  Rank Awareness in Joint Sparse Recovery , 2010, IEEE Transactions on Information Theory.

[32]  Wenjun Zeng,et al.  Efficient Compression of Encrypted Grayscale Images , 2010, IEEE Transactions on Image Processing.

[33]  Eli Biham,et al.  New types of cryptanalytic attacks using related keys , 1994, Journal of Cryptology.

[34]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[35]  Zhaoshui He,et al.  Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering , 2011, IEEE Transactions on Neural Networks.

[36]  K. P. Soman,et al.  Secrecy of Cryptography with Compressed Sensing , 2012, 2012 International Conference on Advances in Computing and Communications.

[37]  Claude E. Shannon,et al.  Communication theory of secrecy systems , 1949, Bell Syst. Tech. J..

[38]  Sudarshan K. Srinivasan,et al.  On the Security of Randomized Arithmetic Codes Against Ciphertext-Only Attacks , 2011, IEEE Transactions on Information Forensics and Security.

[39]  Andrzej Cichocki,et al.  Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation , 2012, IEEE Transactions on Signal Processing.