CLM:differential privacy protection method for trajectory publishing

Hao WANG; Zheng-quan XU; Li-zhi XIONG; Tao WANG

doi:10.11959/j.issn.1000-436x.2017119

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CLM:differential privacy protection method for trajectory publishing

Papers | 更新时间：2024-06-05

- CLM:differential privacy protection method for trajectory publishing
- Journal on Communications Vol. 38, Issue 6, Pages: 85-96(2017)
- 作者机构：
  
  1. 武汉大学测绘遥感信息工程国家重点实验室，湖北武汉 430079
  2. 武汉大学地球空间信息技术协同创新中心，湖北武汉 430079
  3. 南京信息工程大学计算机与软件学院，江苏南京 210044
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(41671443);The Applied Basic Research Program of Wuhan(2016010101010024);Open Funding of NUIST and PAPD(KJR16228);Introducing Talenet of NUIST Program(2016r055)
- DOI：10.11959/j.issn.1000-436x.2017119
  CLC： TP309.2
- Online First：2017-06，
  
  Published：25 June 2017
- 稿件说明：
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Hao WANG, Zheng-quan XU, Li-zhi XIONG, et al. CLM:differential privacy protection method for trajectory publishing[J]. Journal on Communications, 2017, 38(6): 85-96.
DOI：

Hao WANG, Zheng-quan XU, Li-zhi XIONG, et al. CLM:differential privacy protection method for trajectory publishing[J]. Journal on Communications, 2017, 38(6): 85-96. DOI： 10.11959/j.issn.1000-436x.2017119.

摘要

针对现有轨迹差分隐私保护发布方法面临的独立噪声容易被滤除的问题，提出一种轨迹差分隐私发布方法——CLM。CLM 提出一种相关性拉普拉斯机制，利用高斯噪声通过特定的滤波器，产生与原始轨迹序列自相关函数一致的相关性噪声序列，叠加到原始轨迹中并发布。实验结果表明，与现有的轨迹差分隐私保护发布方法相比，CLM能够达到更高的隐私保护强度并能保证较好的数据可用性。

Abstract

In order to solve the problem existing in differential privacy preserving publishing methods that the independent noise was easy to be filtered out

a differential privacy publishing method for trajectory data (CLM)

was proposed.A correlated Laplace mechanism was presented by CLM

which let Gauss noises pass through a specific filter to produce noise whose auto-correlation function was similar with original trajectory series.Then the correlated noise was added to the original track and the perturbed track was released.The experimental results show that the proposed method can achieve higher privacy protection and guarantee better data utility compared with existing differential privacy preserving publishing methods for trajectory data.

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references

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