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个性化搜索中一种基于位置服务的隐私保护方法

张强 王国军

张强, 王国军. 个性化搜索中一种基于位置服务的隐私保护方法[J]. 电子与信息学报, 2018, 40(8): 1998-2005. doi: 10.11999/JEIT171137
引用本文: 张强, 王国军. 个性化搜索中一种基于位置服务的隐私保护方法[J]. 电子与信息学报, 2018, 40(8): 1998-2005. doi: 10.11999/JEIT171137
Qiang ZHANG, Guojun WANG. Privacy Preserving Method Based on Location Service in Personalized Search[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1998-2005. doi: 10.11999/JEIT171137
Citation: Qiang ZHANG, Guojun WANG. Privacy Preserving Method Based on Location Service in Personalized Search[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1998-2005. doi: 10.11999/JEIT171137

个性化搜索中一种基于位置服务的隐私保护方法

doi: 10.11999/JEIT171137
基金项目: 国家自然科学基金(61632009, 61472451),广东省自然科学基金(2017A030308006),广东省高等教育高层次人才计划(2016ZJ01),中南大学中央高校基本科研业务费专项资金 (2017zzts141)
详细信息
    作者简介:

    张强:男,1988年生,博士生,研究方向为隐私保护、个性化搜索

    王国军:男,1970年生,教授,博士生导师,研究方向为云计算、大数据、隐私保护

    通讯作者:

    王国军  csgjwang@gmail.com

  • 中图分类号: TP393

Privacy Preserving Method Based on Location Service in Personalized Search

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: 在基于位置服务的个性化搜索中,利用可信第三方服务器以及对等节点是保护用户隐私的主要方法,但在现实生活中,它们却是不完全可信的。为了解决这一问题,该文提出一种个性化搜索中基于位置服务的隐私保护方法。该方法通过转换用户的位置信息,并根据用户的查询类型生成用户模型,进而形成带有用户位置信息的查询矩阵,然后利用矩阵加密用户的查询,隐藏查询矩阵中的用户信息,最后根据安全内积计算返回相关性得分最高的前K个查询文件给用户。安全性分析表明该方法能有效地保护用户的查询隐私和位置隐私,通过分析与实验表明,该方法大幅度地缩短了索引构建时间,降低了通信开销,同时为用户提供了基于位置的个性化搜索结果,一定程度上解决了移动设备屏幕小带来的弊端。
  • 图  1  基于位置服务的用户隐私保护模型

    图  2  基于位置服务的隐私保护方法流程

    图  3  基于位置服务的隐私保护方法索引构建时间模型图

    图  4  基于位置服务的隐私保护方法用户模型构建图

    图  5  基于位置服务的隐私保护方法查询加密图

    图  6  基于位置服务的隐私保护方法索引搜索图

    表  1  该文中的相关符号描述

    符号 描述 符号 描述
    SK 密钥 $K$ 用户提交的参数 $K$
    ${{p}}$ 明文索引 $a$ 大于0的随机数
    ${{I}}$ 加密后的索引 ${{R}}(i,:)$ 与第 $i$个文件的相关性得分
    $C$ 加密后的文件 ${{s}}$ 分裂指示器
    ${{q}}$ 用户模型或用户查询 $h$ 字典中的总关键词数
    ${{U}}$ 用户模型 $m$ 文件数量
    ${{T}}$ 加密后的查询矩阵 ${{G}}$ 查询点的综合评分矩阵
    $n$ 真实的关键词数 $t$ 随机生成的关键词数
    下载: 导出CSV

    表  2  索引构建过程

    算法 索引构建过程
    输入: ${{G}},{{s}},m,{{M}}_1^{\rm{T}},{{M}}_2^{\rm{T}}$
    输出: ${{I}}$
    (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 ${{I}}$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-12-04
  • 修回日期:  2018-04-20
  • 网络出版日期:  2018-06-07
  • 刊出日期:  2018-08-01

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