Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing

JIANG Wei-jin; WANG Hai-juan; ZHOU Wei; CHEN Yi-lin; WU Yu-ting; HAN Yu-qing

doi:10.12263/DZXB.20230359

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Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing

PAPERS | 更新时间：2025-12-08

- Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing
- ACTA ELECTRONICA SINICA Vol. 51, Issue 10, Pages: 2894-2901(2023)
- 作者机构：
  
  1.湖南工商大学前沿交叉学院,湖南长沙 410205
  2.武汉理工大学计算机与人工智能学院,湖北武汉 430070
  3.湘江实验室,湖南长沙 410205
  4.湖南工商大学计算机学院,湖南长沙 410205
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation Basic Science Center Project(61772196;72088101)
- DOI：10.12263/DZXB.20230359
  CLC： TP309
- Received：21 April 2023，
  
  Revised：2023-08-25，
  
  Published：25 October 2023
- 稿件说明：
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蒋伟进,王海娟,周为等.基于自适应连续时间的群智感知轨迹隐私保护方案[J].电子学报,2023,51(10):2894-2901.

JIANG Wei-jin,WANG Hai-juan,ZHOU Wei,et al.Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing[J].ACTA ELECTRONICA SINICA,2023,51(10):2894-2901.
蒋伟进,王海娟,周为等.基于自适应连续时间的群智感知轨迹隐私保护方案[J].电子学报,2023,51(10):2894-2901. DOI： 10.12263/DZXB.20230359.

JIANG Wei-jin,WANG Hai-juan,ZHOU Wei,et al.Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing[J].ACTA ELECTRONICA SINICA,2023,51(10):2894-2901. DOI： 10.12263/DZXB.20230359.

摘要

针对轨迹差分隐私保护存在的预测精度差、隐私预算分配效用低的问题，本文提出自适应连续时间序列下的群智感知轨迹预测方案.首先在任务分配阶段，为参与者分配轨迹路线；其次引入隐马尔可夫模型（Hidden Markov Model，HMM），对轨迹进行预测；然后使用预分配和自适应分配相结合的综合隐私预算分配方法，降低隐私预算；最后利用拉普拉斯机制，进行位置扰动.实验结果表明，与相关工作相比，所提方法兼顾预测性和低预算性，对群智感知中参与者在轨迹隐私安全保护上具有良好的保护效果.

Abstract

In response to the problems of poor prediction accuracy and low utility of privacy budget allocation in trajectory differential privacy protection

our paper proposes an adaptive trajectory prediction scheme for continuous time series in crowdsensing. Firstly

in the task assignment phase

trajectory routes are assigned to participants. Then

the HMM (Hidden Markov Model) is introduced to predict the trajectories. Next

a comprehensive privacy budget allocation method combining pre-allocation and adaptive allocation is used to reduce the privacy budget. Finally

the laplace mechanism is applied to perturb the locations. Experimental results show that compared with related work

the proposed method achieves a balance between prediction accuracy and low budget requirements

and provides good privacy protection for participants in trajectory privacy security in crowdsensing.

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references

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