CAJ | 학술논문

传统关于轨迹隐私保护的研究大多假设所有轨迹具有相同的隐私需求。然而，现实应用中不同轨迹的隐私需求可能不尽相同，使用相同的隐私保护标准对轨迹进行处理将使所发布轨迹的可用性降低。为此，提出一种可实现个性化轨迹隐私保护的（K，ε）-隐私模型和基于该模型的个性化隐私保护轨迹匿名算法IDU-K。算法在保证发布轨迹数据的信息损失率不超过阈值ε的前提下，采用基于贪心聚类的等价类划分思想对含有不同隐私需求的轨迹集合进行个性化匿名处理。实验对算法IDU-K 的隐私保护有效性及发布数据可用性与同类算法进行比较分析。实验结果表明，算法IDU-K是有效可行的。
전통관우궤적은사보호적연구대다가설소유궤적구유상동적은사수구。연이，현실응용중불동궤적적은사수구가능불진상동，사용상동적은사보호표준대궤적진행처리장사소발포궤적적가용성강저。위차，제출일충가실현개성화궤적은사보호적（K，ε）-은사모형화기우해모형적개성화은사보호궤적닉명산법IDU-K。산법재보증발포궤적수거적신식손실솔불초과역치ε적전제하，채용기우탐심취류적등개류화분사상대함유불동은사수구적궤적집합진행개성화닉명처리。실험대산법IDU-K 적은사보호유효성급발포수거가용성여동류산법진행비교분석。실험결과표명，산법IDU-K시유효가행적。
Most exist works on privacy preserving traj ectory data publishing adopt the same privacy preser-ving standards for all trajectories,without regard to their possibly different privacy requirements.The conse-quence is that the data utility of released trajectory data may be greatly reduced.In order to address this issue, a(K,ε)-privacy model and an algorithm IDU-K for personalized privacy preserving traj ectory data publishing are presented.The key idea of IDU-K is to anonymize the trajectories personally by equivalence partitioning based on greedy clustering while assuring the information loss ratio of the released traj ectory data not more than a thresholdε.Experimental analysis is designed by comparing IDU-K and the traditional algorithm on the effec-tiveness and data utility.Experimental results show that IDU-K is effective and feasible.