Survey of differentially private methods for trajectory data

SUN Xinyue; ZHANG Weizhe; HE Hui; YANG Renyu

doi:10.11959/j.issn.2096-109x.2025027

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Survey of differentially private methods for trajectory data

Comprehensive Reviews | 更新时间：2025-06-30

- Survey of differentially private methods for trajectory data
- Chinese Journal of Network and Information Security Vol. 11, Issue 3, Pages: 1-18(2025)
- 作者机构：
  
  1.哈尔滨工业大学计算学部网络空间安全学院，哈尔滨黑龙江150001
  2.北京航空航天大学软件学院，北京 100191
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62402137);The Postdoctoral Fellowship Program of CPSF(GZC20242205);The Joint Funds of the NSFC(U22A2036);The Shenzhen Colleges and Universities Stable Support Program(GXWD20220817124251002);The Shenzhen Stable Supporting Program(GXWD20231130110352002)
- DOI：10.11959/j.issn.2096-109x.2025027
  CLC： TP309
- Received：01 January 2025，
  
  Revised：2025-04-30，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
孙新越, 张伟哲, 何慧, 等. 轨迹数据的差分隐私保护方法综述[J]. 网络与信息安全学报, 2025, 11(3): 1-18.

Format: SUN X Y, ZHANG W Z, HE H, et al. Survey of differentially private methods for trajectory data[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 1-18.
孙新越, 张伟哲, 何慧, 等. 轨迹数据的差分隐私保护方法综述[J]. 网络与信息安全学报, 2025, 11(3): 1-18. DOI： 10.11959/j.issn.2096-109x.2025027.

Format: SUN X Y, ZHANG W Z, HE H, et al. Survey of differentially private methods for trajectory data[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 1-18. DOI： 10.11959/j.issn.2096-109x.2025027.

摘要

随着传感器和定位技术的快速发展，用户的智能移动设备生成、存储并分享了海量的轨迹数据。这些数据蕴含有价值的个人时空移动特征，商业公司和政府部门可以利用这些特征为用户和社会提供高效、便捷的服务。然而，个人的轨迹数据是私密且敏感的，不合理的数据使用不仅会泄露用户的家庭和工作地址，还会暴露他们的健康状况、经济情况。这种隐私担忧导致人们不愿分享自己的轨迹数据，阻碍了位置服务和应用的发展。为了解决这种隐私担忧问题，差分隐私技术被提出，它不仅能够为用户的敏感数据提供严格可证明的隐私保证，还能保留数据中有价值的信息。具体地，综述了差分隐私技术在保护轨迹数据隐私方面的研究进展；从隐私模型、应用场景和扰动方式等角度对面向轨迹数据的差分隐私方法进行了重点分析；对差分隐私在轨迹数据隐私保护方面的发展方向进行了展望。

Abstract

With the rapid development of sensor and positioning technologies

vast amounts of trajectory data were generated

stored

and shared by users’ smart mobile devices. These data contained valuable personal spatiotemporal mobility features

which could be leveraged by businesses and government agencies to provide efficient and convenient services to users and society. However

individual trajectory data were private and sensitive

and improper data usage could not only expose users’ home and work addresses but also reveal their health status and economic conditions. These privacy concerns led to reluctance in sharing trajectory data

hindering the development of location-based services and applications. To address this issue

a promising solution—differential privacy (DP) technology—was proposed. DP could offer rigorous

provable privacy guarantees for users’ sensitive data while preserving valuable information. The research progress of DP in protecting trajectory data privacy was reviewed. The DP methods for trajectory data were analyzed from perspectives such as privacy models

application scenarios

and perturbation mechanisms. Finally

an outlook on the future development of DP for trajectory data privacy protection was provided.

关键词

Keywords

references

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