Abstract:  In order to enhance the security factor of patients’ private information and reduce the risk of data leakage, a hierarchical encryption algorithm for massive medical information based on chaotic cellular neural network and wavelet transform was proposed under the premise of considering the importance of privacy. The word repetition rate of medical information is calculated by the matching degree of word segmentation and weight matching degree, and similar data in massive information is eliminated, so as to reduce the workload of subsequent information encryption. The importance of data privacy is evaluated by grades such as medical importance, number of visits, and data size, and medical text information and image information are distinguished by data attributes. Using the chaotic features of cellular neural networks, the original medical information is converted into a parameter matrix. Logistic mapping is used to obtain the key chaotic sequence, the medical text information after primary encryption is output, the image signal is analyzed in time domain by wavelet transform to achieve secondary encryption, and the result of secondary encryption is fused to complete the hierarchical encryption of medical information. The experimental results show that the proposed algorithm has the advantages of good encryption effect, fast execution speed and high security factor, and is a suitable solution for the safe storage of medical information.

Key words:  ')">

 , importance of privacy, medical information, hierarchical encryption, pretreatment, chaotic cellular neural network