云存储环境下的多关键字密文搜索方法

doi:10.11772/j.issn.1001-9081.2017071869

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (963 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要针对现有云存储环境下多关键字密文搜索方法效率较低、缺乏自适应能力的问题，提出一种基于改进质量层次聚类的加密云数据多关键字排序搜索（MRSE-IQHC）方法。首先，采用词频-逆向文件频率（TF-IDF）方法和向量空间模型（VSM）构建文件向量；然后，提出一种改进质量层次聚类（IQHC）算法对文件向量聚类，构建文件索引和聚类索引；其次，采用K最近邻（KNN）查询算法对索引加密；最后，采用用户自定义关键字权值的方法构建搜索请求并在密文状态下搜索出前k个最相关的文件。实验结果表明，该方法与加密的云数据多关键字排序搜索（MRSE）方法以及基于层次聚类索引的加密数据多关键字排序搜索（MRSE-HCI）方法相比，在相同的搜索文件数量、返回文件数量、搜索关键字数量条件下搜索时间平均缩短了44.3%和34.2%、32.4%和13.2%、36.9%和19.4%，准确率提升了10.8%和8.6%。所提方法在云存储环境下的多关键字密文搜索中具有较高的搜索效率和准确性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	杨宏宇
	王玥

关键词 ：云存储, 多关键字搜索, 词频-逆向文件频率, 向量空间模型, 聚类, 隐私保护

Abstract：Aiming at the problem of low efficiency and lack of adaptive ability for the existing multi-keyword ciphertext search methods in cloud storage environment, a Multi-keyword Ranked Search over Encrypted cloud data based on Improved Quality Hierarchical Clustering (MRSE-IQHC) method was proposed. Firstly, the document vectors were constructed by Term Frequency-Inverse Document Frequency (TF-IDF) method and Vector Space Model (VSM). Secondly, the Improved Quality Hierarchical Clustering (IQHC) algorithm was proposed to cluster the document vectors, the document index and cluster index were constructed. Thirdly, the K-Nearest Neighbor (KNN) query algorithm was used to encrypt the indexes. Finally, the user-defined keyword weight was used to construct the search request and search for the top k relevant documents in ciphertext state. The experimental results show that compared with the Multi-keyword Ranked Search over Encrypted cloud data (MRSE) method and the Multi-keyword Ranked Search over Encrypted data based on Hierarchical Clustering Index (MRSE-HCI) method, the search time was shortened by 44.3% and 34.2%, 32.4% and 13.2%, 36.9% and 19.4% in the same number of search documents, retrieved documents and search keywords conditions, and the accuracy rate was increased by 10.8% and 8.6%. The proposed method MRSE-IQHC has high search efficiency and accuracy for multi-keyword ciphertext search in cloud storage environment.

Key words： cloud storage multi-keyword search Term Frequency-Inverse Document Frequency (TF-IDF) Vector Space Model (VSM) clustering privacy protection

收稿日期: 2017-08-01

基金资助:国家自然科学基金资助项目（60776807，61179045）；国家科技重大专项（2012ZX03002002）；中国民航科技基金资助项目（MHRD201009，MHRD201205）。

通讯作者: 杨宏宇 E-mail: yhyxlx@hotmail.com

作者简介: 杨宏宇(1969-),男,吉林长春人,教授,博士,CCF会员,主要研究方向:网络信息安全;王玥(1991-),女,甘肃张掖人,硕士研究生,主要研究方向:网络信息安全。

引用本文:

杨宏宇, 王玥. 云存储环境下的多关键字密文搜索方法[J]. 计算机应用, 2018, 38(2): 343-347. YANG Hongyu, WANG Yue. Multi-keyword ciphertext search method in cloud storage environment. Journal of Computer Applications, 2018, 38(2): 343-347.

链接本文:

http://www.joca.cn/CN/10.11772/j.issn.1001-9081.2017071869 或 http://www.joca.cn/CN/Y2018/V38/I2/343

[1]	CAO N, WANG C, LI M, et al. Privacy-preserving multi-keyword ranked search over encrypted cloud data[C]//IEEE INFOCOM 2011:Proceedings of the 30th IEEE International Conference on Computer Communications. Piscataway, NJ:IEEE, 2011:829-837.
[2]	秦志光,包文意,赵洋,等.云存储中一种模糊关键字搜索加密方案[J].信息网络安全,2015(6):7-12. (QIN Z G, BAO W Y, ZHAO Y, et al. A fuzzy keyword search scheme with encryption in cloud storage[J]. Netinfo Security, 2015(6):7-12.)
[3]	HANDA R, CHALLA R K. A cluster based multi-keyword search on outsourced encrypted cloud data[C]//INDIACom 2015:Proceedings of the 2nd International Conference on Computing for Sustainable Global Development. Piscataway, NJ:IEEE, 2015:115-120.
[4]	王雅山.云存储平台中加密数据的多关键字排序搜索技术研究[D].哈尔滨:哈尔滨工业大学,2015:12-38. (WANG Y S. Secure rank-ordered search of multi-keyword in cloud storage platform[D]. Harbin:Harbin Institute of Technology, 2015:12-38.)
[5]	CHEN C, ZHU X, SHEN P, et al. An efficient privacy-preserving ranked keyword search method[J]. IEEE Transactions on Parallel & Distributed Systems, 2016, 27(4):951-963.
[6]	孔振.基于VSM的文本分类系统的设计和实现[D].哈尔滨:哈尔滨工业大学,2014:15-17. (KONG Z. The design and implementation of text classification system based on VSM[D]. Harbin:Harbin Institute of Technology, 2014:15-17.)
[7]	郭文杰,张应辉,郑东.云存储中支持词频和用户喜好的密文模糊检索[J].深圳大学学报(理工版),2015,32(5):532-537. (GUO W J, ZHANG Y H, ZHENG D. Fuzzy search over encrypted data supporting word frequencies and user preferences in cloud storage[J]. Journal of Shenzhen University (Science and Engineering), 2015, 32(5):532-537.)
[8]	杨宏宇,常媛.基于K均值多重主成分分析的App-DDoS检测方法[J].通信学报,2014,35(5):16-24. (YANG H Y, CHANG Y. App-DDoS detection method based on K-means multiple principal component analysis[J]. Journal on Communications, 2014, 35(5):16-24.)
[9]	彭长生.基于Fisher判别的分布式K-Means聚类算法[J].江苏大学学报(自然科学版),2014,35(4):422-427. (PENG C S. Distributed K-Means clustering algorithm based on Fisher discriminant ratio[J]. Journal of Jiangsu University (Natural Science Edition), 2014, 35(4):422-427.)
[10]	WONG W K, CHEUNG D W-L, KAO B, et al. Secure kNN computation on encrypted databases[C]//SIGMOD'09:Proceedings of the 2009 ACM Special Interest Group on Management of Data International Conference on Management of Data. New York:ACM, 2009:139-152.)
[11]	李荣陆.文本分类语料库(复旦)测试语料[EB/OL].[2017-07-06]. http://www.nlpir.org/?action-viewnews-itemid-103. (LI R L. Text categorization corpus (Fudan) test corpus[EB/OL].[2017-07-06]. http://www.nlpir.org/?action-viewnews-itemid-103.)
[12]	JOY E C, KALIANNAN I. Multi keyword ranked search over encrypted cloud data[J]. International Journal of Applied Engineering Research, 2014, 9:7149-7176.
[13]	FU Z, SUN X, LIU Q, et al. Achieving efficient cloud search services:multi-keyword ranked search over encrypted cloud data supporting parallel computing[J]. IEICE Transactions on Communications, 2015, 98(1):190-200.
[14]	YAO L, GU J, GAO Y. Optimized ciphertext retrieval for cloud computing based on dynamic clustering[C]//Proceedings of the 3rd ACM Workshop on Mobile Sensing, Computing and Communication. New York:ACM, 2016:35-39.
[15]	KRISHNA C R, HANDA R. Dynamic cluster based privacy-preserving multi-keyword search over encrypted cloud data[C]//Proceedings of the 20166th Conference on Cloud System and Big Data Engineering. Piscataway, NJ:IEEE, 2016:146-151.