發(fā)布時(shí)間：2018-10-08 18:32

【摘要】：在傳統(tǒng)的基于歐幾里德距離函數(shù)的軌跡相似性計(jì)算過程中,要求軌跡等長且時(shí)間點(diǎn)對(duì)應(yīng),無法度量不等長且有局部時(shí)間偏移的軌跡相似性。因此在構(gòu)造同步軌跡集合過程中產(chǎn)生信息損失較大,影響軌跡數(shù)據(jù)的可用性。為此,通過引進(jìn)一種可以度量不等長且有局部時(shí)間偏移的軌跡間相似性的DTW(dynamic time warping)距離度量函數(shù),提出一種新的軌跡匿名模型——(k,δ,p)-匿名模型,構(gòu)造了DTW-TA(dynamic time warping trajectory anonymity)算法。在合成數(shù)據(jù)集和真實(shí)數(shù)據(jù)集下的實(shí)驗(yàn)結(jié)果表明,該算法在滿足軌跡k-匿名隱私保護(hù)的基礎(chǔ)上,減少了信息損失,提高了軌跡數(shù)據(jù)的可用性。
[Abstract]:In the traditional trajectory similarity calculation based on Euclidean distance function, the trajectory similarity with equal length and time points is required, and the locus similarity with unequal length and local time offset can not be measured. Therefore, the information loss in the process of constructing synchronous track sets is large, which affects the usability of trajectory data. Therefore, by introducing a DTW (dynamic time warping) distance metric function which can measure the similarity between tracks with unequal length and local time offset, a new locus anonymous model- (k, 未 -p) -anonymity model is proposed, and the DTW-TA (dynamic time warping trajectory anonymity) algorithm is constructed. The experimental results on synthetic data sets and real data sets show that the proposed algorithm can reduce the loss of information and improve the usability of trajectory data on the basis of satisfying the path k- anonymity privacy protection.
【作者單位】： 江西理工大學(xué)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61462034,61563019) 江西省教育廳科學(xué)技術(shù)研究項(xiàng)目(GJJ13415) 江西理工大學(xué)科研基金重點(diǎn)課題(NSFJ2014-K11)
【分類號(hào)】：TP309
，

本文編號(hào)：2257832