基于多边缘服务器的个性化搜索隐私保护方法

doi:10.11959/j.issn.1000-436x.2019024

Abstract

Abstract:

In the plaintext environment,users' personalized search results can be obtained through users' interest model and query keywords.However,it may possibly result in the disclosure of sensitive data and privacy,which prevents using sensitive data in cloud search.Therefore,data is generally stored in the form of ciphertext in the cloud server.In the process of cloud search service,users intend to quickly obtain the desired search results from the vast amount of ciphertext.In order to solve the problem,it was proposed that a method of privacy protection based on multiple edge servers in personalized search shall be used.By introducing multiple edge servers and cutting the index as well as the query matrix,the computing relevance scores of partial query and partial file index are achieved on the edge server.The cloud server only needs to get the relevance score on the edge server and make a simple processing that can return to the most relevant Top K files by user query,so as to make it particularly suitable for a large number of users in the massive personalized ciphertext search.Security analysis and experimental results show that this method can effectively protect users’ privacy and data confidentiality.In addition,it can guarantee high efficiency in search to provide better personalized search experience.

Key words: personalized search, privacy protection, edge server, index cutting, searchable encryption

CLC Number:

TP391

Qiang ZHANG,Guojun WANG,Shaobo ZHANG. Method of privacy protection based on multiple edge servers in personalized search[J]. Journal on Communications, 2019, 40(2): 40-50.

Figures/Tables 13

符号	描述
sk	密钥
K	用户提交的参数K
a	大于0的随机数
p ,p_i	明文索引
$I, I_{i}$	加密后的索引
C	加密后的文件集
$R_{i} (j, :)$	第i个边缘服务器上的第 j行索引的相关性得分
$S_{i}$	第i个边缘服务器计算的相关性得分矩阵
$q, q_{i}$	用户查询
n,n_i	关键词数
$U$	用户模型
m	文件数量
$T, T_{i}$	加密后的查询矩阵
S	云服务器上计算获得的相关性得分矩阵
h	边缘服务器数量
key	文件密文解密密钥

References 26

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关键词/个	选择概率
1～500	20%
501～1 000	15%
1 001～1 500	10%
1 501～1 732	1%

兴趣模型	密文按得分降序排序的文件编号
忽略模型	135 85 41 32 34 57 13 69 90 141
考虑模型	90 85 135 32 189 64 41 45 13 199

关键词/个	用户1	用户2	用户3
1～500	20%	12%	5%
501～1 000	15%	20%	12%
1 001～1 500	10%	15%	5%
1 501～1 732	1%	6%	10%

兴趣模型	返回用户的top 10 结果
忽略模型/用户1～用户3	135 85 41 32 34 57 13 69 90 141
考虑模型/用户1	90 85 135 32 189 64 41 45 13 199
考虑模型/用户2	135 85 32 41 189 13 69 181 57 34
考虑模型/用户3	135 57 141 181 69 34 146 85 189 32

文件数/个	本文算法/s	MRSE/s	PRSE/s
2 000	1.72	6 211.11	5 531.48
4 000	3.09	13 921.08	12 094.42
6 000	4.64	20 111.26	18 364.29
8 000	6.01	27 212.51	25 130.54
10 000	7.03	32 243.81	30 360.43

Method of privacy protection based on multiple edge servers in personalized search

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