Differential Privacy Trajectory Data Publishing Based on Orientation Control

Li Yang; Zhou Ying

doi:10.12052/gdutxb.220170

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Differential Privacy Trajectory Data Publishing Based on Orientation Control

更新时间：2026-05-27

- Differential Privacy Trajectory Data Publishing Based on Orientation Control
- Vol. 40, Issue 5, Pages: 56-63(2023)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220170
  CLC： TP391
- Published：2023
- 稿件说明：
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李杨, 周莹. 基于方向控制的差分隐私轨迹数据发布方法[J]. 广东工业大学学报, 2023, 40(5): 56-63.

Li Yang, Zhou Ying. Differential Privacy Trajectory Data Publishing Based on Orientation Control[J]. 2023, 40(5): 56-63.
李杨, 周莹. 基于方向控制的差分隐私轨迹数据发布方法[J]. 广东工业大学学报, 2023, 40(5): 56-63. DOI： 10.12052/gdutxb.220170.

Li Yang, Zhou Ying. Differential Privacy Trajectory Data Publishing Based on Orientation Control[J]. 2023, 40(5): 56-63. DOI： 10.12052/gdutxb.220170.

摘要

随着差分隐私研究及其应用的不断拓展，其在轨迹数据发布的隐私保护领域应用受到了广泛关注，现有研究方法大多采用Kmeans聚类方法对轨迹进行聚类划分，但由于差分隐私约束下的轨迹数据集受到噪声的扰动，导致现有的聚类方法无法保证最后的收敛效果。本文提出了一种基于方向控制的差分隐私保护轨迹数据发布方法。首先，提出了基于SKmeans||聚类的轨迹泛化算法，在聚类迭代过程中针对质心的更新，加入方向控制机制，设计指数机制中的打分函数控制质心的收敛，保证高维数据聚类的质量。其次，设计了一个基于有界阶梯噪声机制的轨迹数据发布算法，其中的有界阶梯噪声机制保证了在隐藏轨迹点真实计数的同时，提高了发布后轨迹数据的可用性。最后，通过实验验证了本文所提出方法的有效性。

Abstract

With the continuous expansion of differential privacy and its applications

its application in the privacy protection field of trajectory data release has received extensive attention. However

most existing research methods use the Kmeans to cluster the trajectory

which cannot guarantee the final convergence due to the fact that the trajectory datasets under differential privacy constraints are usually disturbed by noise. To addrss this

this paper proposes an orientation control-based differential privacy-preserving trajectory data publishing method. Firstly

a trajectory generalization algorithm based on SKmeans|| clustering is proposed

which updates the centroid via a direction control mechanism during iterative process of clustering

and designs a scoring function in the index mechanism to control the convergence of the centroid

such that the quality of high dimensional data clustering can be improved. Secondly

a trajectory data publishing algorithm based on bounded noise mechanism is designed

which improves the availability of trajectory data after publishing. Meanwhile

the bounded noise mechanism ensures the true count of the hidden trajectory. Finally

the effectiveness of the method proposed in this paper is evaluated by experiments.

关键词

Keywords

references

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