Trajectory privacy protection scheme based on differential privacy

Si CHEN; Anmin FU; Mang SU; Huaijiang SUN

doi:10.11959/j.issn.1000-436x.2021168

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Trajectory privacy protection scheme based on differential privacy

Papers | 更新时间：2024-06-05

- Trajectory privacy protection scheme based on differential privacy
- Journal on Communications Vol. 42, Issue 9, Pages: 54-64(2021)
- 作者机构：
  
  1. 南京理工大学计算机科学与工程学院，江苏南京 210094
  2. 南京理工大学后勤服务中心，江苏南京 210094
  3. 中国科学院信息工程研究所，北京 100093
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62072239);Open Foundation of the State Key Laboratory of Information Security of China(2021-MS-07);The Fundamental Research Funds for the Central Universities(30920021129);Special Project of “Higher Education Informatization Research” of China Higher Education Association(2020XXHD06)
- DOI：10.11959/j.issn.1000-436x.2021168
  CLC： TP391
- Online First：2021-09，
  
  Published：25 September 2021
- 稿件说明：
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Si CHEN, Anmin FU, Mang SU, et al. Trajectory privacy protection scheme based on differential privacy[J]. Journal on Communications, 2021, 42(9): 54-64.
DOI：

Si CHEN, Anmin FU, Mang SU, et al. Trajectory privacy protection scheme based on differential privacy[J]. Journal on Communications, 2021, 42(9): 54-64. DOI： 10.11959/j.issn.1000-436x.2021168.

摘要

为了解决现有采样机制和数据混淆方法容易导致公开发布的轨迹数据可用性较低和隐私保护不足的问题，提出了一种基于差分隐私的轨迹隐私保护方案。该方案通过建立新的基于时间泛化和空间分割的高效采样模型，并利用k-means聚类算法进行抽样数据处理，同时借助差分隐私保护机制对轨迹数据进行双重扰动，有效解决了具有强大背景知识的攻击者窃取用户隐私的问题。同时，为适应轨迹数据查询范围的误差边界，设计了有效的数据发布预判机制，保证了发布的轨迹数据的精度。仿真结果表明，与现有的轨迹差分隐私保护方法相比，所提方案在处理效率、隐私保护强度和数据可用性等方面具有明显的优势。

Abstract

To solve the problem that the current sampling mechanism and data obfuscation method may raise insufficient data availability and privacy protection

a trajectory privacy protection scheme based on differential privacy was proposed.A new efficient sampling model based on time generalization and spatial segmentation was presented

and a k-means clustering algorithm was designed to process sampling data.By employing the differential privacy mechanism

the trajectory data was disturbed to solve the user privacy leaking problem caused by the attacker with powerful background knowledge.Simultaneously

to respond to the error boundary of the query range of pandemic

an effective prediction mechanism was designed to ensure the availability of released public track data.Simulation results demonstrate that compared with the existing trajectory differential privacy protection methods

the proposed scheme has obvious advantages in terms of processing efficiency

privacy protection intensity

and data availability.

关键词

Keywords

references

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