考虑隐私重要程度的医疗信息分级加密算法

Hierarchical Encryption Algorithm of Medical Information Considering Importance of Privacy

为增强病人隐私信息安全系数,降低数据泄露风险,在考虑隐私重要程度前提下,提出一种基于混沌细胞神经网络和小波变换的海量医疗信息分级加密算法。利用分词匹配度和权值匹配度计算医疗信息词语重复率,剔除海量信息中相似数据,降低后续信息加密工作量。使用医疗重要性、访问次数、数据大小等分级评估数据隐私重要度,通过数据属性区分医疗文字信息与图像信息。运用细胞神经网络的混沌特征,将原始医疗信息转换为参数矩阵。运用Logistic映射得到密钥混沌序列,输出一次加密后的医疗文字信息,使用小波变换时域分析图像信号达到二次加密,融合二次加密结果完成医疗信息分级加密。实验结果证明,所提算法具有加密效果好、执行速率快、安全系数高等优势,是面向医疗信息安全储存的适宜方案。

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.