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A fast pseudo-stochastic sequential cipher generator based on RBMs

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Abstract

Based on Restricted Boltzmann machines, an improved pseudo-stochastic sequential cipher generator is proposed. It is effective and efficient because of the two advantages: this generator includes a stochastic neural network that can perform the calculation in parallel, that is to say, all elements are calculated simultaneously; unlimited number of sequential ciphers can be generated simultaneously for multiple encryption schemas. The periodicity and the correlation of the output sequential ciphers meet requirements for the design of encrypting sequential data. In the experiment, the generated sequential cipher is used to encrypt images, and better performance is achieved in terms of the key space analysis, the correlation analysis, the sensitivity analysis and the differential attack. To evaluate the efficiency of our method, a comparative study is performed with a prevalent method called “logistic map.” Our approach achieves a better performance on running time estimation. The experimental results are promising as the proposed method could promote the development of image protection in computer security.

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Acknowledgements

This work was supported by Scientific and Technological Research Program of Chongqing Municipal Education Commission (Nos. KJ1501405, KJ1501409); Scientific and Technological Research Program of Chongqing University of Education (Nos. KY201522B, KY201520B); Fundamental Research Funds for the Central Universities (No. XDJK2016E068); Natural Science Foundation of China (No. 61170192) and National High-tech R&D Program (No. 2013AA013801).

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Correspondence to Li Li.

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Hu, F., Xu, X., Peng, T. et al. A fast pseudo-stochastic sequential cipher generator based on RBMs. Neural Comput & Applic 30, 1277–1287 (2018). https://doi.org/10.1007/s00521-016-2753-2

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