個(gè)性化搜索中一種基于位置服務(wù)的隱私保護(hù)方法

張強(qiáng); 王國軍

doi:10.11999/JEIT171137

個(gè)性化搜索中一種基于位置服務(wù)的隱私保護(hù)方法

doi: 10.11999/JEIT171137

張強(qiáng)¹,
王國軍^2, ,

1.
中南大學(xué)信息科學(xué)與工程學(xué)院 ??長沙 ??410083
2.
廣州大學(xué)計(jì)算機(jī)科學(xué)與教育軟件學(xué)院 ??廣州 ??510006

基金項(xiàng)目: 國家自然科學(xué)基金(61632009, 61472451)，廣東省自然科學(xué)基金(2017A030308006)，廣東省高等教育高層次人才計(jì)劃(2016ZJ01)，中南大學(xué)中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金 (2017zzts141)

詳細(xì)信息

作者簡介:
張強(qiáng)：男，1988年生，博士生，研究方向?yàn)殡[私保護(hù)、個(gè)性化搜索

王國軍：男，1970年生，教授，博士生導(dǎo)師，研究方向?yàn)樵朴?jì)算、大數(shù)據(jù)、隱私保護(hù)

通訊作者:
王國軍　 csgjwang@gmail.com

中圖分類號: TP393
計(jì)量
- 文章訪問數(shù): 1762
- HTML全文瀏覽量: 536
- PDF下載量: 34
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-12-04
- 修回日期: 2018-04-20
- 網(wǎng)絡(luò)出版日期: 2018-06-07
- 刊出日期: 2018-08-01

Privacy Preserving Method Based on Location Service in Personalized Search

Qiang ZHANG¹,
Guojun WANG^{2
, ,}

1.
School of Information Science and Engineering, Central South University, Changsha 410083, China
2.
School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China (61632009, 61472451), The Guangdong Provincial Natural Science Foundation (2017A030308006), The High-Level Talents Program of Higher Education in Guangdong Province (2016ZJ01), The Fundamental Research Funds for the Central Universities of Central South University (2017zzts141)

摘要

摘要: 在基于位置服務(wù)的個(gè)性化搜索中，利用可信第三方服務(wù)器以及對等節(jié)點(diǎn)是保護(hù)用戶隱私的主要方法，但在現(xiàn)實(shí)生活中，它們卻是不完全可信的。為了解決這一問題，該文提出一種個(gè)性化搜索中基于位置服務(wù)的隱私保護(hù)方法。該方法通過轉(zhuǎn)換用戶的位置信息，并根據(jù)用戶的查詢類型生成用戶模型，進(jìn)而形成帶有用戶位置信息的查詢矩陣，然后利用矩陣加密用戶的查詢，隱藏查詢矩陣中的用戶信息，最后根據(jù)安全內(nèi)積計(jì)算返回相關(guān)性得分最高的前K個(gè)查詢文件給用戶。安全性分析表明該方法能有效地保護(hù)用戶的查詢隱私和位置隱私，通過分析與實(shí)驗(yàn)表明，該方法大幅度地縮短了索引構(gòu)建時(shí)間，降低了通信開銷，同時(shí)為用戶提供了基于位置的個(gè)性化搜索結(jié)果，一定程度上解決了移動設(shè)備屏幕小帶來的弊端。
- 隱私保護(hù) /
- 個(gè)性化搜索 /
- 位置轉(zhuǎn)換 /
- 安全內(nèi)積計(jì)算
Abstract: For personalized search based on location service, the trusted third-party server and peer node are used as the main method for privacy preserving. However, entirely trusted third-party server or peer node does not exist in real life. In order to address this problem, a method of privacy preserving on the location of mobile users is proposed when using personalized search. The method is used to convert the user’s location information into distance information and generate the user model according to the user’s query type, forming a query matrix with user location information, then the matrix is used to encrypt the user’s query and conceal the user information in the query matrix. Finally, according to the calculation of the security inner product, the K file with the highest relevance score is returned to the user. It is evident from the security analysis that the proposed method can effectively protect the user’s query privacy and location privacy. The analysis and experimental results show that the proposed method can greatly shorten the time of index construction and reduce the communication overhead. While providing users with location based personalized search results, the method is able to remedy the defects of small-screen mobile devices.
- Privacy preserving /
- Personalized search /
- Location conversion /
- Security inner product

HTML全文

圖 1 基于位置服務(wù)的用戶隱私保護(hù)模型

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圖 2 基于位置服務(wù)的隱私保護(hù)方法流程

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圖 3 基于位置服務(wù)的隱私保護(hù)方法索引構(gòu)建時(shí)間模型圖

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圖 4 基于位置服務(wù)的隱私保護(hù)方法用戶模型構(gòu)建圖

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圖 5 基于位置服務(wù)的隱私保護(hù)方法查詢加密圖

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圖 6 基于位置服務(wù)的隱私保護(hù)方法索引搜索圖

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表 1 該文中的相關(guān)符號描述

符號	描述	符號	描述
SK	密鑰	$K$	用戶提交的參數(shù) $K$
${{p}}$	明文索引	$a$	大于0的隨機(jī)數(shù)
${{I}}$	加密后的索引	${{R}}(i,:)$	與第 $i$個(gè)文件的相關(guān)性得分
$C$	加密后的文件	${{s}}$	分裂指示器
${{q}}$	用戶模型或用戶查詢	$h$	字典中的總關(guān)鍵詞數(shù)
${{U}}$	用戶模型	$m$	文件數(shù)量
${{T}}$	加密后的查詢矩陣	${{G}}$	查詢點(diǎn)的綜合評分矩陣
$n$	真實(shí)的關(guān)鍵詞數(shù)	$t$	隨機(jī)生成的關(guān)鍵詞數(shù)

下載: 導(dǎo)出CSV

表 2 索引構(gòu)建過程

算法索引構(gòu)建過程
輸入： ${{G}},{{s}},m,{{M}}_1^{\rm{T}},{{M}}_2^{\rm{T}}$
輸出： ${{I}}$
(1) $p={\rm{diag}}({G});$
(2) $h = n + t;$
(3) ${{tp}} = {\rm{rand(1,1)*ones(1,}}t{\rm{);}}$
(4) for $i = 1:m$ do
(5) 　 ${{{p}}^ * }(i,:) = [{{p}}(i,:){\rm{ }}{{tp}}];$
(6) 　 ${{r}} = {\rm{rand}}(1,h);$
(7) 　 for $j = 1:h$ do
(8)　　 if ${{s}}(j) = = 1$ then
(9)　　　 ${{p}}'(i,j) = {{r}}(1,j);$
(10)　　　 ${{p}}''(i,j) = {{{p}}^ * }(i,j) - {{p}}'(i,j);$
(11)　　　 else
(12)　　　 ${{p}}'(i,j) = {{p}}''(i,j) = {{{p}}^ * }(i,j);$
(13)　　　 end if
(14)　　　 end for
(15) end for
(16) ${{I}} = [{{p}}'{{M}}_1^{\rm{T}},{{p}}''{{M}}_2^{\rm{T}}]$
(17) return ${{I}}$

下載: 導(dǎo)出CSV

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