计算机应用研究2017，Vol.34Issue(11)：3428-3431,4.DOI:10.3969/j.issn.1001-3695.2017.11.051
k-匿名改进模型下的LCSS-TA轨迹匿名算法
LCSS-TA trajectory anonymity algorithm based on improved k-anonymity model
摘要
Abstract
In traditional trajectory similarity calculation based on the Euclidean distance metric function,position of each point in the trajectory are required to have a corresponding point.The existence of noises could lead to track a larger distance deviation,reduce the trajectory similarity,increase trajectory information loss.In order to solve this problem,this paper designed LCSS-TA (longest common subsequences trajectory anonymity)algorithm combining with LCSS (longest common subsequences)distance function and (k,δ)-anonymity model.The algorithm could decrease the greater distance of the noises might to lead by mapping the distance between trajectory locations points to 0 or 1.In synthetic data set and data set with noise,the experiment results show that the algorithm can reduce noises interference and decrease the trajectory information loss on the basis of meeting with k-anonymity privacy protection.关键词
轨迹数据/隐私保护/噪声点/LCSS距离度量函数/(k,δ)-匿名模型Key words
trajectory data/privacy protection/noises/LCSS distance metric function/(k, δ)-anonymity model分类
信息技术与安全科学引用本文复制引用
郑剑,刘聪..k-匿名改进模型下的LCSS-TA轨迹匿名算法[J].计算机应用研究,2017,34(11):3428-3431,4.基金项目
国家自然科学基金资助项目(61462034,61563019) （61462034,61563019）
江西省教育厅科学技术研究资助项目(GJJ13415) （GJJ13415）
江西理工大学科研基金重点课题(NSFJ2014-K11) （NSFJ2014-K11）
