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A two-layer networks-based audio encryption/decryption scheme via fixed-time cluster synchronization

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Abstract

In this paper, an audio encryption/decryption scheme based on the cluster synchronization of two-layer complex networks within fixed-time is proposed. The two-layer network model composed of multiple encryption/decryption units, in which the nodes in each unit behave identical dynamics, while the nodes in different ones may behave different dynamics, is adopted. These encryption/decryption units can be processed in parallel, such that the speed for information encryption/decryption can be greatly improved. The drive network is used for encryption, and the response network is used for decryption. The encryption/decryption process mainly includes two steps: time-domain scrambling and encryption function operation. The chaotic sequences generated by multiple different chaotic systems are used as the keys, and the symmetric encryption method based on the synchronization of complex networks is used, which can make the key space be larger and the security be higher. Moreover, the fixed-time strategy is introduced such that the encryption/decryption time is controllable and predictable, this is a very important indicator in practical applications. The feasibility and effectiveness of the given audio encryption scheme are verified by the theoretical analysis and numerical simulation results.

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Data Availability

The data sets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 61803322 and 11747141), in part by the Natural Science Foundation of Hunan Province (No. 2018JJ3512), and in part by the Scientific Research Fund of Hunan Provincial Education Department (No. 21B0178).

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Correspondence to Lili Zhou.

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Zhou, L., Li, X., Tan, F. et al. A two-layer networks-based audio encryption/decryption scheme via fixed-time cluster synchronization. Soft Comput 26, 9761–9774 (2022). https://doi.org/10.1007/s00500-022-07335-x

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