基于DTW距离度量函数的DTW-TA轨迹匿名算法  

DTW-TA trajectory anonymity algorithm based on DTW distance measurement function

作　　者：郑剑[1] 刘聪[1] 

机构地区：[1]江西理工大学信息工程学院,江西赣州341000

出　　处：《计算机应用研究》2017年第8期2459-2463,2468,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(61462034;61563019);江西省教育厅科学技术研究项目(GJJ13415);江西理工大学科研基金重点课题(NSFJ2014-K11)

摘　　要：在传统的基于欧几里德距离函数的轨迹相似性计算过程中,要求轨迹等长且时间点对应,无法度量不等长且有局部时间偏移的轨迹相似性。因此在构造同步轨迹集合过程中产生信息损失较大,影响轨迹数据的可用性。为此,通过引进一种可以度量不等长且有局部时间偏移的轨迹间相似性的DTW(dynamic time warping)距离度量函数,提出一种新的轨迹匿名模型——(k,δ,p)-匿名模型,构造了DTW-TA(dynamic time warping trajectory anonymity)算法。在合成数据集和真实数据集下的实验结果表明,该算法在满足轨迹k-匿名隐私保护的基础上,减少了信息损失,提高了轨迹数据的可用性。In the process of traditional trajectory similarity calculation based on the Euclidean distance function, it required the trajectory should be same length and time point correspondence, a similarity between the local time migration trajectories couldwt be measured. So the information loss of process of the synchronous collection construction is bigger, affected the traj- ectory data availability. Therefore, this paper constructed the DTW-TA anonymity algorithm by introducing a range to measure unequal length and local time migration trajectory similarity DTW distance metric function and putt forward a new track anony- mous model：（k,8,p）-anonymity model. Under the synthetic data and real data sets of the experimental results show that the algorithm reduces the loss of information, enhances the usability of trajectory data meet the basis of k-anonymity model privacy protection.

关 键 词：轨迹数据 隐私保护 DTW距离度量函数 (k δ p)-匿名模型 数据可用性 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]