| 摘要: |
| 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.针对这一问题,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.以《软件学报》最近10年的论文作为样本进行了实验,数据集中选取2 900篇文档和4 800个关键词.实验结果显示:当文档集个数呈指数增长时,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. |
| 关键词: 云计算 密文检索 多关键字排序检索 相似查询树 云安全 |
| DOI：10.13328/j.cnki.jos.004998 |
| 分类号: |
| 基金项目:广东电网有限责任公司信息中心大数据环境下的数据安全研究项目(K-GD2014-1019);中国科学院战略性先导科技专项(XDA06040601);新疆维吾尔自治区科技专项(201230121) |
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| Efficient Ciphertext Search Method Based on Similarity Search Tree |
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TIAN Xue1, ZHU Xiao-Jie1, SHEN Pei-Song1, CHEN Chi1, ZOU Hong2
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1.State Key Laboratory of Information Security, (Institute of Information Engineering, The Chinese Academy of Sciences), Beijing 100093, China;2.Information Center, Guangdong Power Grid Company Limited, Guangzhou 510000, China
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| Abstract: |
| With extensive applications of cloud computing, data capacity of data centers 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, this paper proposes an efficient ciphertext search method based on similarity search tree (MRSE-SS) that can handle big data volume. The proposed approach clusters the documents based on the max distance between the cluster center and its members, constructs an n-dimensional hyper sphere by using the cluster center as the center of sphere and the max distance as radius, and then gradually clusters 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, thus improving the efficiency of ciphertext search. An experiment is conducted using the collection set built from the recent ten years' JC publications, containing about 2900 documents with nearly 4800 keywords. The results show that the presented approach can reach a linear computational complexity against exponential size of document collection. In addition, the retrieved documents have a better relationship with each other than by traditional methods. |
| Key words: cloud computing ciphertext search multi-keyword ranked search similarity search tree cloud security |
