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Abstract: In response to the strong correlation between the chaotic system state and initial state and parameters in traditional chaotic encryption algorithms, which may lead to periodicity in chaotic sequences, the chaos long short-term memory (chaos-LSTM) model is constructed by combining chaotic systems with LSTM neural networks. The chaos sequence proliferation (CSP) algorithm is constructed to address the problem that the limited computational accuracy of computers can lead to periodicity in long chaotic sequences, making them unsuitable for encrypting objects with large amounts of data. By combining the chaos-LSTM model and CSP algorithm, a geographic information encryption system is proposed. First, the chaos-LSTM model is used to output chaotic sequences with high spectral entropy (SE) complexity. Then, a shorter chaotic sequence is selected and proliferated using the CSP algorithm to generate chaotic proliferation sequences that match the encrypted object; a randomness analysis is conducted and testing is performed on it. Finally, using geographic images as encryption objects, the chaotic proliferation sequence, along with the scrambling and diffusion algorithms, are combined to form the encryption system, which is implemented on the ZYNQ platform. The system’s excellent confidentiality performance and scalability are proved by software testing and hardware experiments, making it suitable for the confidentiality peers of various encryption objects with outstanding application value.

基于Chaos-LSTM与混沌序列增殖的地理信息数据加密系统

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