计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 308-312.doi: 10.11896/jsjkx.210300231
李昊, 曹书瑜, 陈亚青, 张敏
LI Hao, CAO Shu-yu, CHEN Ya-qing, ZHANG Min
摘要: 近年来,基于位置服务的应用逐渐开始普及,它在为人们生活提供便利的同时,也对个人隐私造成了巨大威胁。现有研究表明,在具备大量历史轨迹数据的情况下,攻击者能够从匿名化的轨迹数据集中识别出用户身份与轨迹的链接关系。然而,这些相关研究都面临着数据稀疏和数据质量差这两方面的问题。数据稀疏指用户的轨迹往往只分布在局部区域,同时缺乏与自然语言处理领域一样规模庞大的语料库;数据质量差指轨迹中的位置点往往存在采样率低和噪音大的问题。针对上述问题,文中提出了一种基于注意力机制的用户轨迹识别模型,包括位置嵌入模块、基于注意力的位置转移特征编码模块和轨迹用户识别模块。位置嵌入模块用于将原始轨迹中位置点的空间关系嵌入到位置向量中;基于注意力的位置转移特征编码模块用于提取轨迹中位置点间的转移依赖关系,生成轨迹的表征向量;轨迹用户识别模块用于对轨迹表征向量的用户身份进行预测。最后,在Gowalla和Geolife数据集上进行了实验验证,实验结果表明,所提方案有效缓解了轨迹数据稀疏和数据质量差带来的问题,能够提高轨迹的用户身份识别率。
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