基于信息熵抑制的大数据特征隐匿性加密仿真

针对传统大数据特征加密方法中大数据精度较差、加密耗时较长的问题,提出基于信息熵抑制的大数据特征隐匿性加密方法。分析信息熵抑制相关理论,采用信息熵映射函数对大数据分类,将信息熵抑制过程转化为计算2个文件相似性的过程,并根据余弦相似度公式以及Hamming距离值计算数据相似度,完成数据消冗。在完成数据消冗的基础上,分析分组密码方法和ECC加密方法,结合两种方法得到大数据特征隐匿性加密处理计算公式,实现基于信息熵抑制的大数据特征隐匿性加密。实验研究结果表明,所提方法能够有效去除冗余数据,提升大数据精度,加快数据加密的处理速度,高效完成大数据加密过程。

Hidden Encryption Simulation of Big Data Features Based on Information Entropy Suppression

Aiming at the problem that the big data accuracy is poor and the encryption takes a long time in the traditional big data feature encryption method, a large data feature occult encryption method based on information entropy suppression is proposed. We analyze the theory of information entropy suppression, classify big data by information entropy mapping function, transform the information entropy suppression process into the process of calculating the similarity of two files, and calculate the data similarity according to the cosine similarity formula and Hamming distance value to remove the data redundancy. On the basis of removing the data redundancy, the block cipher method and the ECC encryption method are analyzed, and the two methods are used to obtain the cryptic encryption processing formula of big data features, and the occult encryption of big data features based on information entropy suppression is realized. Simulation results show that the proposed method can effectively remove redundant data, improve the accuracy of big data, speed up the processing speed of data encryption, and efficiently complete the big data encryption process.