基于特征匹配的快速降维排序搜索方法  

Fast Dimensionality Reduction Sorting Search Method Based on Feature Matching

作　　者：徐惠 铁治欣 舒莹 XU Hui;TIE Zhi-xin;SHU Ying(School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China;Keyi College, Zhejiang Sci-Tech University, Shaoxing 312369, China)

机构地区：[1]浙江理工大学信息学院,浙江杭州310018 [2]浙江理工大学科技与艺术学院,浙江绍兴312369

出　　处：《计算机与现代化》2022年第2期85-91,96,共8页Computer and Modernization

基　　金：国家自然科学基金资助项目(61170110);浙江省自然科学基金资助项目(LY13F020043);浙江省教育厅科研项目(21030074-F)。

摘　　要：在大数据时代背景下,越来越多的用户或者企业将大量的数据上传至云端存储以便减轻本地存储的压力和获得高效的数据共享服务管理,由此可搜索加密技术应运而生,检索效率与保证数据安全一直是研究的热点。因此,本文提出一种基于特征匹配的快速降维排序搜索方法(DRFM)。通过提出的特征得分算法,创建每一篇文档的索引特征向量;通过提出的匹配得分算法,创建查询关键词的查询匹配向量。使用K-L变换算法对所有文档索引特征向量以及查询匹配向量进行降维,提高算法效率。理论分析与实验结果表明所提的方案高效且可行。In the era of big data,more and more users or enterprises upload a large amount of data to cloud storage so as to reduce the pressure of local storage and obtain efficient management of data sharing services.As a result,searchable encryption technology arises at the right moment.Retrievable efficiency and guarantee of data security have always been the focus of research.Therefore,a fast dimensionality reduction sorting search method based on feature matching(DRFM)is proposed.Through the proposed feature score algorithm,the index feature vector of each document is created;through the proposed matching score algorithm,the query matching vector of query keywords is created.The K-L transform algorithm is used to reduce the dimensions of all document index feature vectors and query matching vectors to improve the efficiency of the algorithm.Theoretical analysis and experimental results show that the proposed scheme is efficient and feasible.

关 键 词：特征匹配 加密 降维 索引特征向量 查询匹配向量 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]