MEC下基于模糊敏感度量的个性化轨迹隐私保护方法

蒋忠元; 房梦欣; 王启舟; 马建峰; 李兴华

doi:10.11959/j.issn.1000-436x.2026077

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MEC下基于模糊敏感度量的个性化轨迹隐私保护方法

学术论文 | 更新时间：2026-05-07

- MEC下基于模糊敏感度量的个性化轨迹隐私保护方法
- Personalized trajectory privacy protection method based on fuzzy sensitivity quantification in MEC
- 通信学报 2026年47卷第4期页码：145-162
- 作者机构：
  
  1.西安电子科技大学网络与信息安全学院，陕西西安 710126
  2.紫金山实验室，江苏南京 211111
  3.西安交通大学电信学部，陕西西安 710049
- 作者简介：
  
  [ "蒋忠元（1988- ），男，陕西榆林人，博士，西安电子科技大学教授、博士生导师，主要研究方向为空天地一体化网络、6G网络安全、数据与智能安全等。" ]
  [ "房梦欣（2000- ），女，山东烟台人，西安电子科技大学硕士生，主要研究方向为数据安全与隐私保护、深度学习。" ]
  [ "王启舟（1994- ），男，陕西汉中人，博士，西安交通大学助理研究员，主要研究方向为并行与分布式系统、海量存储、算法设计与分析等。" ]
  [ "马建峰（1963- ），男，陕西西安人，博士，西安电子科技大学教授、博士生导师，主要研究方向为无线网络安全、移动智能系统安全等。" ]
  [ "李兴华（1978- ），男，河南南阳人，博士，西安电子科技大学教授、博士生导师，主要研究方向为网络与信息安全、安全协议形式化等。" ]
- 基金信息：
  
  陕西省杰出青年科学基金资助项目(2025JC-JCQN-085);陕西省重点研发计划基金资助项目(2023-YBGY-270)
- DOI：10.11959/j.issn.1000-436x.2026077
  中图分类号： TP309
- 收稿：2026-01-04，
  
  修回：2026-03-15，
  
  录用：2026-03-16，
  
  纸质出版：2026-04-20
- 稿件说明：
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蒋忠元,房梦欣,王启舟等.MEC下基于模糊敏感度量的个性化轨迹隐私保护方法[J].通信学报,2026,47(04):145-162.

Jiang Zhongyuan,Fang Mengxin,Wang Qizhou,et al.Personalized trajectory privacy protection method based on fuzzy sensitivity quantification in MEC[J].Journal on Communications,2026,47(04):145-162.
蒋忠元,房梦欣,王启舟等.MEC下基于模糊敏感度量的个性化轨迹隐私保护方法[J].通信学报,2026,47(04):145-162. DOI： 10.11959/j.issn.1000-436x.2026077.

Jiang Zhongyuan,Fang Mengxin,Wang Qizhou,et al.Personalized trajectory privacy protection method based on fuzzy sensitivity quantification in MEC[J].Journal on Communications,2026,47(04):145-162. DOI： 10.11959/j.issn.1000-436x.2026077.

摘要

针对现有轨迹隐私保护方法普遍缺乏对用户个性化隐私需求的考虑，且传统的加噪机制容易造成数据可用性下降的问题，提出了一种基于Laplace机制的个性化动态轨迹隐私保护方法。首先，利用模糊数学构建位置点敏感度量化模型，融合位置语义特征与用户隐私偏好，获得个性化的敏感度评估结果；随后，基于轨迹中位置点单元的隐私权重动态分配隐私预算，并使用Laplace机制实现动态轨迹扰动，从而在高敏感区域提供更强保护，在低敏感区域保持更高数据可用性。实验结果表明，与现有轨迹差分隐私保护方法相比，所提方法在隐私保护效果与轨迹数据可用性方面均具有显著优势。

Abstract

To address the limitations of existing trajectory privacy protection methods

which generally neglect users’ personalized privacy requirements and suffer from utility degradation under traditional noise-injection mechanisms

a personalized dynamic trajectory privacy protection method based on the Laplace mechanism was proposed. First

a fuzzy sensitivity quantification model was developed to characterize the semantic sensitivity of each location point by jointly considering spatial contextual features and individual privacy preferences

thereby enabling personalized sensitivity assessment. Then

a dynamic privacy budget allocation strategy was designed to distribute privacy budgets according to the sensitivity levels of trajectory points

upon which the Laplace mechanism was applied to generate adaptive perturbations. This enabled stronger protection for highly sensitive regions while maintaining higher data fidelity for low-sensitivity areas. Experimental results show that

compared with representative differential privacy-based trajectory perturbation methods

the proposed method achieves superior privacy preservation performance and significantly enhances the utility of perturbed trajectory data.

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references

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