基于轨迹位置形状相似性的隐私保护算法

王超; 杨静; 张健沛

doi:10.11959/j.issn.1000-436x.2015043

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基于轨迹位置形状相似性的隐私保护算法

学术论文 | 更新时间：2024-06-05

- 基于轨迹位置形状相似性的隐私保护算法
- Privacy preserving algorithm based on trajectory location and shape similarity
- 通信学报 2015年36卷第2期页码：144-157
- 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：
  
  [ "王超（1988-），男，河北沧州人，哈尔滨工程大学博士生，主要研究方向为数据库与知识工程、数据挖掘、隐私保护。" ]
  [ "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库与知识工程、数据挖掘、隐私保护、软件理论等。" ]
  [ "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，主要研究方向为企业智能计算、数据库与知识工程、数据挖掘、社会网络、软件理论等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61370083);国家自然科学基金资助项目(61073043);国家自然科学基金资助项目(61073041);高等学校博士学科点专项科研基金资助项目(20112304110011);高等学校博士学科点专项科研基金资助项目(201223041100);黑龙江省自然科学基金资助项目(F200901);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(2011RFXXG015)
- DOI：10.11959/j.issn.1000-436x.2015043
  中图分类号： TP391.7
- 网络出版日期：2015-02，
  
  纸质出版日期：2015-02-25
- 稿件说明：
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王超, 杨静, 张健沛. 基于轨迹位置形状相似性的隐私保护算法[J]. 通信学报, 2015,36(2):144-157.

Chao WANG, Jing YANG, Jian-pei ZHANG. Privacy preserving algorithm based on trajectory location and shape similarity[J]. Journal on communications, 2015, 36(2): 144-157.
王超, 杨静, 张健沛. 基于轨迹位置形状相似性的隐私保护算法[J]. 通信学报, 2015,36(2):144-157. DOI： 10.11959/j.issn.1000-436x.2015043.

Chao WANG, Jing YANG, Jian-pei ZHANG. Privacy preserving algorithm based on trajectory location and shape similarity[J]. Journal on communications, 2015, 36(2): 144-157. DOI： 10.11959/j.issn.1000-436x.2015043.

摘要

为了降低轨迹数据发布产生的隐私泄露风险，提出了多种轨迹匿名算法。然而，现有的轨迹匿名算法在计算轨迹相似性时忽略了轨迹的形状因素对轨迹相似性的影响，因此产生的匿名轨迹集合的可用性相对较低。针对这一问题，提出了一种新的轨迹相似性度量模型，在考虑轨迹的时间和空间要素的同时，加入了轨迹的形状因素，可以在多项式时间内计算定义在不同时间跨度上的轨迹的距离，能够更加准确、快速地度量轨迹之间的相似性；在此基础上，提出了一种基于轨迹位置形状相似性的隐私保护算法，最大限度地提高了聚类内部轨迹的相似性，并且使用真实的原始位置信息形成数据“面罩”，满足了轨迹k-匿名，在有效地保护轨迹数据的同时，提高了轨迹数据的可用性；最后，在合成轨迹数据集和真实轨迹数据集上的实验结果表明，本算法花费更少的时间代价，具有更高的数据可用性。

Abstract

In order to reduce the privacy disclosure risks when trajectory data is released

a variety of trajectories anonymity methods were proposed.However

while calculating similarity of trajectories

the existing methods ignore the impact that the shape factor of trajectory has on similarity of trajectories

and therefore the produced set of trajectory anonymity has a lower utility.To solve this problem

a trajectory similarity measure model was presented

considered not only the time and space elements of the trajectory

but also the shape factor of trajectory.It is computable in polynomial time

and can calculate the distance of trajectories not defined over the same time span.On this basis

a greedy clustering and data mask based trajectory anonymization algorithm was presented

which maximized the trajectory similarity in the clusters

and formed data ＂mask＂ which is formed by fully accurate true original locations information to meet the trajectory k-anonymity.Finally

experimental results on a synthetic data set and a real-life data set were presented; our method offer better utility and cost less time than comparable previous proposals in the literature.

关键词

Keywords

references

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