Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing

Mingfeng ZHAO; Chen LEI; Yang ZHONG; Jinbo XIONG

doi:10.11959/j.issn.2096-109x.2021016

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Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing

Papers | 更新时间：2024-06-03

- Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing
- Chinese Journal of Network and Information Security Vol. 7, Issue 1, Pages: 157-166(2021)
- 作者机构：
  
  1. 福建师范大学数学与信息学院，福建福州 350117
  2. College of Engineering and Computing, Georgia Southern University, GA, 30458
  3. 福建省网络安全与密码技术重点实验室，福建福州 350117
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2021016
  CLC： TP309.2
- Online First：2021-02，
  
  Published：15 February 2021
- 稿件说明：
移动端阅览
Mingfeng ZHAO, Chen LEI, Yang ZHONG, et al. Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing[J]. Chinese Journal of Network and Information Security, 2021, 7(1): 157-166.
DOI：

Mingfeng ZHAO, Chen LEI, Yang ZHONG, et al. Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing[J]. Chinese Journal of Network and Information Security, 2021, 7(1): 157-166. DOI： 10.11959/j.issn.2096-109x.2021016.

摘要

移动边缘群智感知中，用户执行感知任务采集数据所包含的隐私量是动态变化且不直观的，数据上传亦缺乏隐私风险预警值，提出一种动态隐私度量（DPM）模型。给出用户参与感知任务所获数据的结构化表示并转化成原始数值矩阵，引入隐私属性偏好与时效性因素实现对该矩阵的权重叠加，以度量数据所含隐私的动态变化，基于权重叠加后的矩阵合理计算用户个性化隐私阈值，并进行差分隐私处理。在此基础上，设计一种隐私度量模型评价机制。仿真结果表明，模型是有效且合理的，根据所给范例，差分隐私处理后的数据效用达到0.7，随噪声水平增加，隐私保护程度（PDD）可显著提升，适应物联网移动边缘群智感知范式。

Abstract

To tackle the problems of users not having intuitive cognition of the dynamic privacy changes contained in their sensing data in mobile edge crowdsensing (MECS) and lack of personalized privacy risk warning values in the data uploading stage

a dynamic privacy measurement (DPM) model was proposed.A structured representation of data obtained by a user participating in a sensing task was introduced and was transformed it into a numerical matrix.Then privacy attribute preference and timeliness were presented to quantify the dynamic privacy changes of data.With this

personalized privacy thresholds of users based on the numerical matrix were reasonably calculated.Finally

differential privacy processing was performed on the numerical matrix

and a model evaluation system was designed for the proposed model.The simulation results show that the DPM model was effective and practical.According to the given example

a data utility of approximately 0.7 can be achieved

and the degree of privacy protection can be significantly improved as the noise level increases

adapting to the MECS of IoT.

关键词

Keywords

references

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