移动边缘群智感知动态隐私度量模型与评价机制

赵明烽; Lei Chen; 钟洋; 熊金波

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

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移动边缘群智感知动态隐私度量模型与评价机制

学术论文 | 更新时间：2024-06-03

- 移动边缘群智感知动态隐私度量模型与评价机制
- Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing
- 网络与信息安全学报 2021年7卷第1期页码：157-166
- 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：
  
  [ "赵明烽（1996- ），男，江苏张家港人，福建师范大学硕士生，主要研究方向为移动数据安全和隐私保护。" ]
  [ "Lei Chen（1978- ），男，陕西西安人，美国佐治亚州南方大学副教授，主要研究方向为网络安全、信息安全、云计算与大数据安全等。" ]
  [ "钟洋（1995- ），男，湖南湘西人，福建师范大学硕士生，主要研究方向为安全深度学习。" ]
  [ "熊金波（1981- ），男，湖南益阳人，福建师范大学教授、博士生导师，主要研究方向为网联自动驾驶车辆的安全与隐私、物联网安全、大数据安全、隐私保护。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61872088);国家自然科学基金;The National Natural Science Foundation of China(U1905211);国家自然科学基金资助项目;The National Natural Science Foundation of China(61872090);福建省自然科学基金资助项目;The Natural Science Foundation of Fujian Province(2019J01276);贵州省公共大数据重点实验室开放课题;The Guizhou Provincial Key Laboratory of Public Big Data Research Fund(2019BDKFJJ004)
- DOI：10.11959/j.issn.2096-109x.2021016
  中图分类号： TP309.2
- 网络首发：2021-02，
  
  纸质出版：2021-02-15
- 稿件说明：
移动端阅览
赵明烽, Lei Chen, 钟洋, 等. 移动边缘群智感知动态隐私度量模型与评价机制[J]. 网络与信息安全学报, 2021,7(1):157-166.

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.
赵明烽, Lei Chen, 钟洋, 等. 移动边缘群智感知动态隐私度量模型与评价机制[J]. 网络与信息安全学报, 2021,7(1):157-166. DOI： 10.11959/j.issn.2096-109x.2021016.

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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