弱关联字符型数据的密文检索模型优化仿真

Simulation of Model Optimization for Weak Correlation Ciphertext Character Data Retrieval

研究弱关联数据库字符型密文检索优化问题。对于加密后的字符型关系数据库,由于字符型密文数据关联性较弱,原有明文字符顺序性特征也会被弱化,给查询操作带来了极大的困难。传统的字符型数据库密文检索模型,无法全面分析加密后的密文字符的排名大小以及概率值大小,获取的字符型密文检索在语义上具有歧义性,效率低。提出采用模糊粗糙集的弱关联字符型数据密文检索模型。提取字符型数据的属性特征,从而为密文检索提供依据。根据模糊粗糙集相关理论,建立密文目标匹配度检索机制,对弱关联字符型数据进行密文检索。实验结果表明,利用改进算法进行字符型密文检索,能够在字符型数据关联性较差的情况下准确的检索到目标对象,有效的提高检索过程中的查全率和查准率,从而满足不同领域对于信息的需求。

This paper studied the optimization problem of character ciphertext retrieval in weak correlation database. An weak association character data ciphertext retrieval model based on fuzzy rough sets was proposed. Firstly, the property characteristic of character data was extracted to provide the basis to retrieve the ciphertext. Then based on fuzzy rough set theory, the degree of matching ciphertext target retrieval mechanism was established to complete the weak correlation character data ciphertext retrieval. Experimental results show that the improved algorithm for character cipher text retrieval can accurately search the target object under the condition of the poor correlation char- acter data and effectively improve the retrieval recall ratio and precision ratio in the process, so as to meet the infor- mation requirements of different areas.