基于差分隐私的轨迹隐私保护方案

陈思; 付安民; 苏铓; 孙怀江

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

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基于差分隐私的轨迹隐私保护方案

学术论文 | 更新时间：2024-06-05

- 基于差分隐私的轨迹隐私保护方案
- Trajectory privacy protection scheme based on differential privacy
- 通信学报 2021年42卷第9期页码：54-64
- 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：
  
  [ "陈思（1987− ），女，湖北襄阳人，南京理工大学博士生，主要研究方向为大数据、隐私保护等" ]
  [ "付安民（1981− ），男，湖北咸宁人，博士，南京理工大学教授，主要研究方向为物联网安全、机器学习与隐私保护等" ]
  [ "苏铓（1987− ），女，内蒙古翁牛特旗人，博士，南京理工大学副教授，主要研究方向为云安全、访问控制与权限管理等" ]
  [ "孙怀江（1968− ），男，陕西西安人，博士，南京理工大学教授，主要研究方向为神经网络与机器学习等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62072239);信息安全国家重点实验室开放基金资助项目(2021-MS-07);中央高校基本科研业务费专项资金资助项目(30920021129);中国高等教育学会“高等教育信息化研究”专项课题基金资助项目(2020XXHD06)
- DOI：10.11959/j.issn.1000-436x.2021168
  中图分类号： TP391
- 网络出版日期：2021-09，
  
  纸质出版日期：2021-09-25
- 稿件说明：
移动端阅览
陈思, 付安民, 苏铓, 等. 基于差分隐私的轨迹隐私保护方案[J]. 通信学报, 2021,42(9):54-64.

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.
陈思, 付安民, 苏铓, 等. 基于差分隐私的轨迹隐私保护方案[J]. 通信学报, 2021,42(9):54-64. DOI： 10.11959/j.issn.1000-436x.2021168.

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

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