电子学报Issue(11)：2338-2344,7.DOI:10.3969/j.issn.0372-2112.2015.11.029
基于稀疏化最小生成树聚类的个性化轨迹隐私保护算法
The Sparse Minimum Spanning Tree Clustering Based Personalized Trajectory Privacy Protection Algorithm
摘要
Abstract
The existing trajectory anonymity methods can not reflect the trajectory internal and external characteristics infor-mation well,and ignore personalized privacy requirements of moving objects.To solve these problems,we propose a new similarity measure model of trajectory structure,which considers the trajectory internal and external characteristics information of direction、speed、angle and location.On this basis,we propose the sparse minimum spanning tree clustering based personalized trajectory priva-cy protection algorithm.It reduces runtime by sparse methods,and generates an approximate optimal trajectory k-anonymity set by greedy strategy.Finally,the results showed our new similarity measure model of trajectory structure can calculate distance of trajec-tories more accurately,and our method offers better utility and costs less time than previous proposals in the literature.关键词
轨迹相似性/个性化轨迹 k-匿名/稀疏化/最小生成树聚类/k-节点划分Key words
trajectory similarity/personalized trajectory k-anonymity/sparse methods/minimum spanning tree clustering/k-node partition分类
信息技术与安全科学引用本文复制引用
王超,杨静,张健沛..基于稀疏化最小生成树聚类的个性化轨迹隐私保护算法[J].电子学报,2015,(11):2338-2344,7.基金项目
国家自然科学基金（No．61370083，No．61073041，No．61073043）；高等学校博士学科点专项科研基金（No．20112304110011， No．20122304110012）；哈尔滨市科技创新人才研究专项资金 （）
