基于图划分的个性化轨迹隐私保护方法

杨静; 张冰; 张健沛; 谢静

doi:10.11959/j.issn.1000-436x.2015053

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基于图划分的个性化轨迹隐私保护方法

学术论文 | 更新时间：2024-06-05

- 基于图划分的个性化轨迹隐私保护方法
- Personalized trajectory privacy preserving method based on graph partition
- 通信学报 2015年36卷第3期页码：1-11
- 作者机构：
  
  哈尔滨工程大学计算机科学与技术学院，黑龙江哈尔滨 150001
- 作者简介：
  
  [ "杨静（1962-），女，黑龙江哈尔滨人，哈尔滨工程大学教授、博士生导师，主要研究方向为数据挖掘、隐私保护等。" ]
  [ "张冰（1986-），女，黑龙江哈尔滨人，哈尔滨工程大学博士生，主要研究方向为隐私保护、人工智能。" ]
  [ "张健沛（1956-），男，黑龙江哈尔滨人，哈尔滨工程大学教授、博士生导师，主要研究方向为社会网络、数据挖掘。" ]
  [ "谢静（1986-），女，湖北随州人，哈尔滨工程大学博士生，主要研究方向为数据挖掘、隐私保护。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61370083);国家自然科学基金资助项目;The National Natural Science Foundation of China(61073043);国家自然科学基金资助项目;The National Natural Science Foundation of China(61073041);高等学校博士学科点专项科研基金资助项目;The Research Fund for the Doctoral Program of Higher Education of China(20112304110011);高等学校博士学科点专项科研基金资助项目;The Research Fund for the Doctoral Program of Higher Education of China(20122304110012);哈尔滨市科技创新人才研究专项基金（优秀学科带头人）资助项目;The Harbin Special Funds for Technological Innovation Research(2011RFXXG015)
- DOI：10.11959/j.issn.1000-436x.2015053
  中图分类号： TP309.2
- 网络出版日期：2015-03，
  
  纸质出版日期：2015-03-25
- 稿件说明：
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杨静, 张冰, 张健沛, 等. 基于图划分的个性化轨迹隐私保护方法[J]. 通信学报, 2015,36(3):1-11.

Jing YANG, Bing ZHANG, Jian-pei ZHANG, et al. Personalized trajectory privacy preserving method based on graph partition[J]. Journal of communications, 2015, 36(3): 1-11.
杨静, 张冰, 张健沛, 等. 基于图划分的个性化轨迹隐私保护方法[J]. 通信学报, 2015,36(3):1-11. DOI： 10.11959/j.issn.1000-436x.2015053.

Jing YANG, Bing ZHANG, Jian-pei ZHANG, et al. Personalized trajectory privacy preserving method based on graph partition[J]. Journal of communications, 2015, 36(3): 1-11. DOI： 10.11959/j.issn.1000-436x.2015053.

摘要

针对用户对轨迹匿名数据的个性化需求，提出一种基于轨迹间夹角和位置重合的(s

λ)-覆盖个性化轨迹间关联构建方法，并根据轨迹间距离和方向度量轨迹间边权，以构造规模可变的个性化轨迹图模型。同时，将轨迹k-匿名集的构建转化为轨迹图划分问题，提出了一种基于贪心策略寻找近似最优的k条轨迹构建轨迹k-匿名集的方法。通过对比实验，在合成轨迹数据集上验证了所提算法的有效性和合理性。

Abstract

A (s

λ)-overlap was proposed which based on the angle and location between two trajectories to construct the relationship between them

and also measure the weight between two trajectories by trajectory angle and distance

to-gether to construct the personal trajectory graph model. Then a greedy partition method to was proposed construct trajec-tory k-anonymization sets by finding the approximate optimal k trajectories. Experiment results on synthetic dataset show the effectiveness and reasonableness of proposed method.

关键词

Keywords

references

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