Abstract
In this paper neural network implementation of the modified secret sharing scheme based on a matrix projection is offered. Transition from a finite simple Galois field to a complex field allows to reduce by 16 times memory size, necessary for storage of the precalculated constants. Implementation of the modified secret sharing scheme based on a matrix projection with use of the neural network of a finite ring for execution of modular arithmetical addition and multiplication operations in a finite field allows to save on average 30% of the device area and increases the speed of scheme’s work on average by 17%.
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References
Bai, L.: A reliable \(\left(k, n\right)\) image secret sharing scheme. In: Autonomic and Secure Computing 2nd IEEE International Symposium on Dependable, pp. 31–36 (2006)
Bai, L.: A strong ramp secret sharing scheme using matrix projection. In: International Symposium on World of Wireless, Mobile and Multimedia Networks, WoWMoM 2006 (2006)
Bai, L., Zou, X.: A Proactive Secret Sharing Scheme in matrix projection method. Int. J. Security and Networks 4(4), 201–209 (2009)
Blakley, G.: Safeguarding crypographic keys. In: Proceedings of the AFIPS 1979 National Computer Conference, vol. 48, pp. 313-317 (1997)
Chervyakov, N.I.: The conveyor neural network of a finite ring. Patent RU 2317584 from (30.05.2008)
Chervyakov, N.I., Babenko, M.G., Lyakhov, P.A., Lavrinenko, I.N.: An Approximate Method for Comparing Modular Numbers and its Application to the Division of Numbers in Residue Number Systems. Cybernetics and Systems Analysis 50(6), 977–984 (2014)
Chervyakov, N.I., Galkina, V.A., Strekalov, U.A., Lavrinenko, S.V.: Neural network of a finite ring. Patent RU 2279132 from (07.08.2003)
Chervyakov, N.I., Sakhnyuk, P.A., Shaposhnikov, V.A., Makokha, A.N.: Neurocomputers in residual classes. M.: Radiotechnique, p. 272 (2003)
Dong, X., Zhang, Y.: A multi-secret sharing scheme based on general linear groups. In: International Conference on Information Science and Technology (ICIST), pp. 480–483 (2013)
Esposito, R., Mountney, J., Bai, L., Silage, D.: Parallel architecture implementation of a reliable \(\left(k, n\right)\) image sharing scheme. In: 14th IEEE International Conference on Parallel and Distributed Systems, pp. 27–34 (2008)
Galushkin, A.I: The theory of neural networks. M.: INGNR, p. 416 (2000)
Gayoso, C.A., Gonzalez, C., Arnone, L., Rabini, M., Castineira Moreira, J.: Pseudorandom number generator based on the residue number system and its FPGA implementation. In: 7th Argentine School of Micro-Nanoelectronics, Technology and Applications (EAMTA), pp. 9–14 (2013)
Jenkins, W.K., Krogmeier, J.V.: The Design of dual-mode complex signal processors based on Quadratic Modular Number Codes. IEEE Transactions on Circuits and Systems 34, 354–364 (1987)
Jullien, G.A., Krishnan, R., Miller, W.C.: Complex digital Signal Processing over ftnite rings. IEEE Transactions on Circuits and Systems 34, 365–377 (1987)
Krishnan, R., Jullien, G.A., Miller, W.C.: Complex digital signal processing using quadratic Residue Number Systems. IEEE Transactions on ASSP 34, 166–177 (1986)
Patil, S., Deshmukh, P.: Enhancing Security in Secret Sharing with Embedding of Shares in Cover Images. International Journal of Advanced Research in Computer and Communication Engineering 3(5), 6685–6688 (2014)
Patil, S., Deshmukh, P.: Verifiable image secret sharing in matrix projection using watermarking. In: International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), pp. 225–229 (2014)
Patil, S., Rana, N., Patel, D., Hodge, P.: Extended Proactive Secret Sharing using Matrix Projection Method. International Journal of Scientific & Engineering Research 4(6), 2024–2029 (2013)
Shamir, A.: How to share a secret. Communications of the ACM 22(11), 612–613 (1979)
Zhang, D., Zhang, D.: Parallel VLSI Neural System Designs, vol. 257. Springer-Verlag, Berlin (1998)
Zhang, C.N., Yun, D.Y.: Parallel designs for Chinese remainder conversion. In: IEEE 16-th International Conference on Parallel Processing - ICPP, pp. 557–559 (1987)
Zang, D., Jullien, G.A., Miller, W.C.: A neural-like approach to finite ring computation. IEEE Transactions on Circuits and Systems 37(8), 1048–1052 (1990)
Zhang, D., Jullien, G.A., Miller, W.C.: VLSI implementations of neural-like networks for finite ring computations. In: Proceedings of the 32nd Midwest Symposium on Circuits and Systems, vol. 1, pp. 485–488 (1989)
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Chervyakov, N.I., Babenko, M.G., Kucherov, N.N., Garianina, A.I. (2015). The Effective Neural Network Implementation of the Secret Sharing Scheme with the Use of Matrix Projections on FPGA. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_1
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