Abstract
In this paper, a secure and efficient Blind Source Separation (BSS) based cryptosystem is presented. The use of BSS in audio and image cryptography in wireless networks has attracted more attention. A BSS based cryptosystem consists of three main parts: secret data, secret keys, and mixing matrix. In this paper, we propose a new design to create a proper mixing matrix in BSS based cryptosystem. We offer a mathematical criterion to select mixing matrix elements before encryption. The proposed criterion gives a simple way to attach the secret sources to keys, which makes source separation very hard for the adversary. Versus, we show that using the random mixing matrix can lead to data security loss. The attacks used for security tests in this paper are "Differential Attack" and "Denoising Attack," which are among the most effective in this field. These attacks will apply to cryptosystems based on the random and the proposed mixing matrix. The visual results of the attacks in the experiments will show that the "proposed mixing matrix based cryptosystem" will be more secure than the "random mixing matrix based cryptosystem." We also used the correlation coefficient criterion to compare the two cryptosystems more accurately. According to the experiments of this paper, the "proposed mixing matrix based cryptosystem" vs. the "random mixing matrix based cryptosystem" was able to reduce the adversary's source extraction quality rate from about 76% to 16%, on average.
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Menezes, A. J., Oorschot, P. C. V., & Vanstone, S. A. (1996). Handbook of applied cryptography. . FL: CRC Press.
Smid, M. E., & Branstad, D. K. (1988). The data encryption standard: Past and future. Proceeding of the IEEE, 76, 550–559.
Daemen, J., & Rijmen, V. (2002). The design of rijndael: AES-the advanced encryption standard. . Berlin: Springer-Verlag.
Kamali, S. H., Shakerian, R., Hedayati, M., & Rahmani, M. (2010). A New Modified Version of Advanced Encryption Standard Based Algorithm for Image Encryption. In International Conference on Electronics and Information Engineering, ser. ICEIE2010, Kyoto, Japan, 1:141–145
Liu, S., Sun, J., & Xu, Z. (2009). An improved image encryption algorithm based on chaotic system. Journal of Computers, 4(11), 1091–1100.
Lin, Q. H., Tin, F. L., Mei, T. M., & Liang, H. L. (2004). A Speech Encryption Algorithm Based on Blind Source Separation. In International Conference on Communications, Circuits and Systems, ser. ICCCAS2004, Chengdu, China, 2:1:1013–1017
Kohmura, S., Togawa, T., & Otani, T. (2017). Source Separation Based on Transfer Function between Microphones and its Dispersion. In: Computing and Communication Workshop and Conference ser. CCWC 1-6
Abbas, N. A. (2015). Image encryption based on independent component analysis and arnold’s cat map. Egyptian Informatics Journal, 17(1), 139–146.
Zhao, H., He, S., Chen, Z., & Zhang, X. (2014). Dual key speech encryption algorithm based underdetermined BSS. Hindawi The Scientific World Jornal, 2014, 751–758.
Sadr, A., & Okhovat, R. S. (2015). An implementing consideration for the key in a BSS-based cryptosystem. Springer Wireless Personal Communication, 80(1), 17–28.
Ridha, O. A. L. A., Jawad, G. N., & Kadhim, S. F. (2018). Modified blind source separation for securing end-to-end mobile voice calls. IEEE Communications Letters, 22(10), 2072–2075.
Li, S., Li, C., Lo, K. T., & Chen, G. (2008). Cryptanalyzing an encryption scheme based on blind source separation. IEEE Transactions on circuit and systems, 55(4), 1055–1063.
ElSafty, A. H., Tolba, M. F., Said, L. A., Madian, A. H., & Radwan, A. G. (2020). Hardware realization of a secure and enhanced s-box based speech encryption engine. Springer Analog Integrated Circuits and Signal Processing. https://doi.org/10.1007/s10470-020-01614-z.
Farhati, A., Aicha, A. B. & Bouallegue, R. (2018). Decryption of BSS Based Encrypted Speech Without A Priori Knowledge of the Key Signal. In: The 4th International Conference on Advanced Technologies for Signal and Image Processing, ser. ATSIP'2018, Sousse, Tunisia, 1–4
Tazehkand, B., & Tinati, M. (2010). Underdetermined blind mixing matrix estimation using STWP analysis for speech source signals. Wireless Sensor Network, 2(11), 854–860.
Reju, V. G., Koh, S. N., & Soon, I. Y. (2009). An algorithm for mixing matrix estimation in instantaneous blind source separation. Elsevier Signal Processing, 89(9), 1762–1773.
Li, Y., Nie, W., Ye, F., & Lin, Y. (2016). A mixing matrix estimation algorithm for underdetermined blind source separation. Springer Circuits, Systems, and Signal Processing, 35(9), 3367–3379.
Li, Y., Nie, W., & Ye, F. (2015). A complex mixing matrix estimation algorithm based on single source point. Springer Circuits, Systems, and Signal Processing, 34(11), 3709–3723.
Guo, Q., Ruan, G., & Na, P. (2017). Underdetermined mixing matrix estimation algorithm based on single source point. Springer Circuits, Systems, and Signal Processing, 36(11), 4453–4467.
Chen, P., Peng, P., Zhen, L., Luo, Y., & Xiang, Y. (2017). Underdetermined blind separation by combining sparsity and independence of sources. IEEE Access, 5, 21731–21742.
Eqlimi, E., Makkiabadi, B., Samadzadehaghdam, N., Khajehpour, H., Mohagheghian, F., & Sanei, S. (2018). A novel underdetermined source recovery algorithm based on k-sparse component analysis. Springer Circuits, Systems, and Signal Processing, 38(3), 1264–1286.
Wei, S., Wang, F. & Jiang, D. (2019). Sparse Component Analysis Based on an Improved Ant K-means Clustering Algorithm for Underdetermined Blind Source Separation. In: IEEE 16th International Conference on Networking, Sensing and Control, ser. ICNSC, 200-205
Hyvärinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Elsevier Neural Networks, 13, 411–430.
Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626–634.
Pal, M., Roy, R., Basu, J., & Bepari, M. S. (2013). Blind Source Separation: A Review and Analysis. International Conference Oriental COCOSDA held jointly with Conference on Asian Spoken Language Research and Evaluation, ser. (pp. 1–5). O-COCOSDA/CASLRE.
Comon, P. (1994). Independent component analysis, a new concept. Elsevier. Signal Processing, 36(3), 287–314.
Bell, A. J., & Sejnowski, T. J. (1995). An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.
Lin, Q.H., Yin, F.L., & Liang, H. (2005). Blind Source Separation-Based Encryption of Images and Speeches. Proceedings of the Second international conference on Advances in neural networks, ser. ISNN'05 2:544–549.
Lin, Q. H., Yin, F. L., Mei, T. M., & Liang, H. (2008). A blind source separation based method for multiple images encryption. Elsevier Image and Vision Computing, 26, 788–798.
Sadr, S., & Okhovat, R. S. (2015). Security in the speech cryptosystem based on blind sources separation. Springer Multimedia Tools and Applications, 74(21), 9715–9728.
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Aslani, M.R., Shamsollahi, M.B. & Nouri, A. Improving data protection in BSS based secure communication: mixing matrix design. Wireless Netw 27, 4747–4758 (2021). https://doi.org/10.1007/s11276-021-02609-y
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DOI: https://doi.org/10.1007/s11276-021-02609-y