传统的欧几里德距离度量函数计算轨迹相似性时,要求轨迹的每个位置点都要有对应点.由于噪声点的存在,导致轨迹距离出现较大偏差,降低轨迹相似性,增加轨迹的信息损失.针对这一问题,结合LCSS(最长公共子序列)距离函数和(k,δ)-匿名模型设计了LCSS-TA(最长公共子序列轨迹匿名)算法.该算法通过将轨迹位置点之间的距离映射成0或1来减小噪声点可能导致的较大距离.在合成数据集和含噪声的数据集下的实验结果表明,提出的算法在满足轨迹k-匿名隐私保护的基础上,可以有效降低噪声干扰,减少轨迹的信息损失.%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.
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