Privacy-Preserving Top-k Keyword Similarity Search over Outsourced Cloud Data  

作　　者：TENG Yiping CHENG Xiang SU Sen WANG Yulong SHUANG Kai 

机构地区：[1]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications

出　　处：《China Communications》2015年第12期109-121,共13页中国通信（英文版）

基　　金：supported partly by the following funding agencies:the National Natural Science Foundation(No.61170274);the Innovative Research Groups of the National Natural Science Foundation(No.61121061);the National Key Basic Research Program of China (No.2011CB302506);Youth Scientific Research and Innovation Plan of Beijing University of Posts and Telecommunications(No. 2013RC1101)

摘　　要：In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.

关 键 词：similarity keyword preserving cloud collection privacy validity files ranking separate 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]