Abstract
In 1999, Guo et al. proposed a new probabilistic symmetric probabilistic encryption scheme based on chaotic attractors of neural networks. The scheme is based on chaotic properties of the Overstoraged Hopfield Neural Network (OHNN). The approach bridges the relationship between neural network and cryptography. However, there are some problems in their scheme: (1) exhaustive search is needed to find all the attractors; (2) the data expansion in the paper is wrongly derived; (3) problem exists on creating the synaptic weight matrix. In this letter, we propose a symmetric probabilistic encryption scheme based on Clipped Hopfield Neural Network (CHNN), which solves the above mentioned problems. Furthermore, it keeps the length of the ciphertext equals to that of the plaintext.
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Leung, K.C., Li, S.L., Cheng, L.M. et al. A Symmetric Probabilistic Encryption Scheme Based On CHNN Without Data Expansion. Neural Process Lett 24, 93–105 (2006). https://doi.org/10.1007/s11063-006-9006-9
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DOI: https://doi.org/10.1007/s11063-006-9006-9