移动边缘群智感知动态隐私度量模型与评价机制

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

Abstract

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.

Key words: dynamic privacy measurement, personalized privacy threshold, differential privacy, model evaluation, mobile edge crowdsensing

CLC Number:

TP309.2

Mingfeng ZHAO, Chen LEI, Yang ZHONG, Jinbo XIONG. Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing[J]. Chinese Journal of Network and Information Security, 2021, 7(1): 157-166.

Figures/Tables 4

任务周期	数据属性
任务周期	TA₁	TA ₂	…	TA_k
TC₁	Data_1,1	Data _1,2	…	Data _{1, k}
TC ₂	Data_2,1	Data _2,2	…	Data _{2, k}
$⋮$	$⋮$	$⋮$	$⋱$	$⋮$
TC_n	Data_n,1	Data _n,2	…	Data _{n, k}

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