基于相似查询树的快速密文检索方法

随着云计算的广泛应用,数据中心的数据量急速增加,同时,用户文档通常包含隐私敏感信息,需要先加密然后上传到云服务器,面对如此大量的密文数据,现有技术在大数据量的密文数据上的检索效率很低.针对此问题,本文提出在大数据下的基于相似查询树的密文检索方法(MRSE-SS),该方法通过设置聚类中心和成员之间的最大距离对文档向量进行聚类,并把中心向量看成n维超球体的球心,最大距离作为半径,再逐步将小聚类聚合成大聚类.使用该方法构建的密文文档集合,在查询阶段仅需检索查询向量相邻的聚类即可获得理想的查询结果集合,从而提高了密文检索的效率.本文还以《软件学报》期刊最近10年的论文作为样本进行了实验,数据集中选取2900篇文章和4800个关键词,实验结果显示,当文档集个数呈指数增长的时候,检索时间仅呈线性增长,并且检索结果的关联性比传统检索方法更强.

Efficient Ciphertext Search Method Based on Similarity Search Tree

Following the wide application of cloud computing, data capacity of the data center has grown rapidly. Furthermore, document information, which usually contains user's sensitive information, needs to be encrypted before being outsourced to data centers. Faced with such a large amount of ciphertext data, current techniques have low search efficiency in this scenario. Aiming at solving this problem, our paper proposes an efficient ciphertext search method based on similarity search tree (MRSE-SS) on account of big data volume. The proposed approach clusters the documents based on the max distance between the cluster center and its members. And construct an n-dimensional hyper sphere by using the cluster center as the center of sphere, the max distance as radius, and then gradually cluster small clusters into large clusters. In the search phase of the ciphertext document collection constructed by this method, the ideal retrieval results can be obtained only by searching the query vector's neighboring clusters, and thus this method can improve the efficiency of ciphertext search. An experiment has been conducted using the collection set built from the recent ten years' JC publications, choosing about 2900 documents with nearly 4800 keywords. The results show that this approach can reach a linear computational complexity against exponential size of document collection. In addition, retrieved documents have a better relationship with each other than traditional methods.