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三维空间位置服务中智能语义位置隐私保护方法

闵明慧 杨爽 胥俊怀 李鑫 李世银 肖亮 彭国军

闵明慧, 杨爽, 胥俊怀, 李鑫, 李世银, 肖亮, 彭国军. 三维空间位置服务中智能语义位置隐私保护方法[J]. 电子与信息学报, 2024, 46(6): 2627-2637. doi: 10.11999/JEIT230708
引用本文: 闵明慧, 杨爽, 胥俊怀, 李鑫, 李世银, 肖亮, 彭国军. 三维空间位置服务中智能语义位置隐私保护方法[J]. 电子与信息学报, 2024, 46(6): 2627-2637. doi: 10.11999/JEIT230708
MIN Minghui, YANG Shuang, XU Junhuai, LI Xin, LI Shiyin, XIAO Liang, PENG Guojun. Intelligent Semantic Location Privacy Protection Method for Location Based Services in Three-Dimensional Spaces[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2627-2637. doi: 10.11999/JEIT230708
Citation: MIN Minghui, YANG Shuang, XU Junhuai, LI Xin, LI Shiyin, XIAO Liang, PENG Guojun. Intelligent Semantic Location Privacy Protection Method for Location Based Services in Three-Dimensional Spaces[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2627-2637. doi: 10.11999/JEIT230708

三维空间位置服务中智能语义位置隐私保护方法

doi: 10.11999/JEIT230708
基金项目: 国家自然科学基金(62101557, 62371451, U21A20444),徐州市基础研究计划项目-青年科技人才项目(KC23022),中国博士后科学基金(2022M713378),中央高校基本科研业务费专项资金(2042022kf0021)
详细信息
    作者简介:

    闵明慧:女,讲师,研究方向为无线通信、网络安全、隐私保护等

    杨爽:女,硕士生,研究方向为位置隐私保护、强化学习

    胥俊怀:男,硕士生,研究方向为位置隐私保护、强化学习

    李鑫:男,硕士生,研究方向为位置隐私保护、强化学习

    李世银:男,教授,研究方向为煤矿信息化、移动目标定位等

    肖亮:女,教授,研究方向为无线通信、网络安全、强化学习等

    彭国军:男,教授,研究方向为网络安全、恶意代码、可信软件等

    通讯作者:

    李世银 lishiyin@cumt.edu.cn

  • 中图分类号: TN929.5

Intelligent Semantic Location Privacy Protection Method for Location Based Services in Three-Dimensional Spaces

Funds: The National Natural Science Foundation of China (62101557, 62371451, U21A20444), Xuzhou Basic Research Plan Project-Young Scientific and Technological Talent Project (KC23022), China Postdoctoral Science Foundation (2022M713378), The Fundamental Research Foundations for the Central Universities (2042022kf0021)
  • 摘要: 针对大型医院、商场及其他3维(3D)空间位置服务中敏感语义位置(如药店、书店等)隐私泄露问题,该文研究了基于3D空间地理不可区分性(3D-GI)的智能语义位置隐私保护方法。为摆脱对特定环境和攻击模型的依赖,该文利用强化学习(RL)技术实现对用户语义位置隐私保护策略的动态优化,提出基于策略爬山算法(PHC)的3D语义位置扰动机制。该机制通过诱导攻击者推断较低敏感度的语义位置来减少高敏感语义位置的暴露。为解决复杂3D空间环境下的维度灾难问题,进一步提出基于近端策略优化算法(PPO)的3D语义位置扰动机制,利用神经网络捕获环境特征并采用离线策略梯度方法优化神经网络参数更新,提高语义位置扰动策略选择效率。仿真实验结果表明,所提方法可提升用户的语义位置隐私保护性能和服务体验。
  • 图  1  系统模型

    图  2  基于PPO的3D语义位置扰动机制

    图  3  实验设置图

    图  4  不同语义位置隐私保护机制的动态性能

    图  5  不同地图尺寸下语义位置隐私保护机制的平均性能

    图  6  不同攻击者先验知识准确性下语义位置隐私保护机制的平均性能

    图  7  不同机制下实际位置与攻击者推断位置敏感度水平的概率分布

    表  1  符号含义

    符号 含义
    $ {\varepsilon ^{(k)}} $ 隐私预算
    $ {{\boldsymbol{d}}^{(k)}}/{c^{(k)}}/{l^{(k)}} $ 实际地理位置/语义位置/语义位置敏感度
    $ {\tilde {\boldsymbol{d}}^{(k)}}/{\tilde c^{(k)}}/{\tilde l^{(k)}} $ 扰动地理位置/语义位置/语义位置敏感度
    $ {\hat {\boldsymbol{d}}^{(k)}}/{\hat c^{(k)}}/{\hat l^{(k)}} $ 推断地理位置/语义位置/语义位置敏感度
    $ {p^{(k)}} $ 隐私水平
    $ {q^{(k)}} $ 服务质量(QoS)损失
    $ {u^{(k)}} $ 用户的效益
    下载: 导出CSV

    1  基于PHC 的3D语义位置扰动机制

     初始化$ Q $表,$ V $表及$ \pi $表,系统参数$\alpha $, $\gamma $, $\delta $, $ {d^{\left( 1 \right)}} $, $ {c^{\left( 1 \right)}} $, $ {l^{\left( 1 \right)}} $,
     $ {\varpi ^{\left( 0 \right)}} $;设置学习迭代次数。
     (1) For $k = 1,2, \cdots $, do
     (2)  观察当前系统状态$ {s^{\left( k \right)}} = \left[ {{{\boldsymbol{d}}^{\left( k \right)}},{c^{\left( k \right)}},{l^{\left( k \right)}},{\varpi ^{\left( {k - 1} \right)}}} \right] $
     (3)  根据$\pi $表选择位置扰动策略$ {{\boldsymbol{a}}^{(k)}} $
     (4)  根据伽马分布$ \varGamma \left( {3,{1 \mathord{\left/ {\vphantom {1 \varepsilon }} \right. } \varepsilon }} \right) $,产生预算对应的$ r $
     (5)  通过式(6)获得扰动位置$ {\tilde {\boldsymbol{d}}^{\left( k \right)}} $,并根据地图信息获取$ {\tilde c^{(k)}} $
     (6)  根据扰动位置$ ({\tilde {\boldsymbol{d}}^{(k)}},{\tilde c^{(k)}}) $请求LBS
     (7)  通过式(7)获取效益$ {u^{\left( k \right)}} $
     (8)  通过式(8)更新$ Q({{\boldsymbol{s}}^{(k)}},{{\boldsymbol{a}}^{(k)}}) $
     (9)  通过式(9)更新$ V({{\boldsymbol{s}}^{(k)}}) $
     (10) 通过式(10)更新$ \pi ({{\boldsymbol{s}}^{(k)}},{\boldsymbol{a}}) $
     (11) End
    下载: 导出CSV

    2  基于PPO的3D语义位置扰动机制

     初始化系统参数和网络参数$\gamma $, $\delta $, ${d^{(1)}}$, ${c^{(1)}}$, ${l^{(1)}}$, $ {\varpi ^{(0)}} $, $ {\theta ^{(0)}} $,
     $ {\phi ^{(0)}} $
     (1)  For $k = 1,2, \cdots, $ do
     (2)  观察当前系统状态$ {s^{(k)}} = \left[ {{d^{\left( k \right)}},{c^{\left( k \right)}},{l^{\left( k \right)}},{\varpi ^{\left( {k - 1} \right)}}} \right] $
     (3)  将状态$ {{\boldsymbol{s}}^{(k)}} $输入到Actor网络得到$ {{\boldsymbol{\mu}} ^{(k)}} $和$ {{\boldsymbol{\xi}} ^{(k)}} $
     (4)  通过式(10)得到$ {\pi _\theta }({\boldsymbol{a}}|{{\boldsymbol{s}}^{(k)}}) $
     (5)  根据$ {\pi _\theta }({\boldsymbol{a}}|{{\boldsymbol{s}}^{(k)}}) $选择扰动策略$ {{\boldsymbol{a}}^{(k)}} $
     (6)  扰动位置的获取参考算法1中的步骤(4)–步骤(5)
     (7)  根据扰动位置$ ({\tilde d^{(k)}},{\tilde c^{(k)}}) $请求LBS
     (8)  通过式(7)进行效益评估
     (9)  将经验序列$ {{\boldsymbol{\varPsi}} ^{(k)}} = ({{\boldsymbol{s}}^{(k)}},{{\boldsymbol{a}}^{(k)}},{u^{(k)}},{{\boldsymbol{s}}^{(k + 1)}}) $存入经验存
        储池中
     (10) If then
     (11) 从经验池中抽取小批量经验值输入到Actor和Critic网络中
     (12) 通过式(12)计算优势函数$ \hat A({{\boldsymbol{s}}^{(k)}},{{\boldsymbol{a}}^{(k)}}) $
     (13) 通过式(13)更新Actor网络参数$ \theta $
     (14) 通过式(14)更新Critic网络参数$ \phi $
     (15) End
     (16) End
    下载: 导出CSV

    表  2  仿真过程的超参数设置

    参数 PHCLP机制 PPOLP机制
    学习率$ \alpha $ 0.5 0.001/0.003
    (Actor/Critic)
    折扣因子$ \gamma $ 0.9 0.9
    截断系数$ \sigma $ - 0.1
    batch-size - 32
    激活函数 - Adam
    神经网络隐藏层数(Actor/Critic) - 2层/3层
    隐藏的单元数(Actor/Critic) - 8,8/8,8,8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-15
  • 修回日期:  2024-01-11
  • 网络出版日期:  2024-01-25
  • 刊出日期:  2024-06-30

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