Balancing trajectory privacy and data utility using a personalized anonymization model
Balancing trajectory privacy and data utility using a personalized anonymization model
复制标题
使用个性化匿名模型平衡轨迹隐私和数据效用
DOI：
10.1016/j.jnca.2013.03.010
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发表时间：
2014-02-01
影响因子：
8.7
通讯作者：
Li, Xinghua
中科院分区：
文献类型：
--
作者：
Gao, Sheng;Ma, Jianfeng;Li, Xinghua
随着基于位置的服务（LBS）的广泛使用，由位置服务提供商收集的轨迹的数量动态地增长。一方面，挖掘和分析这些时空轨迹可以帮助制定与移动相关的战略规划;另一方面，每个轨迹的知识可以被对手用来识别用户的敏感信息，并导致不可预测的伤害。轨迹k-匿名的概念是从位置k-匿名扩展而来的，位置k-匿名已被广泛用于解决这个问题。轨迹k-匿名的主要挑战是选择一个轨迹k-匿名集。然而，现有的匿名方法忽略了轨迹的相似性和方向，假设它对隐私的影响很小。因此，它不能提供一个更好的轨迹k-匿名集。本文提出用轨迹角来评价轨迹的相似性和方向性，并在轨迹距离的基础上构造匿名区域。考虑到不同场景下轨迹隐私和数据效用比例的偏好设置不同，提出了一种个性化匿名化模型来选择轨迹k-匿名集。实验结果表明，在不同的轨迹隐私和数据效用要求比例下，该方法都能提供一个有效的轨迹k-匿名集，且效率略有降低. (C)2013爱思唯尔有限公司保留所有权利。
With the widespread use of location-based services (LBS), the number of trajectories gathered by location service providers is dynamically growing. On the one hand, mining and analyzing these spatiotemporal trajectories can help to work out a mobile-related strategic planning; on the other hand, knowledge of each trajectory can be used by adversaries to identify the user's sensitive information and lead to an unpredictable harm. The concept of trajectory k-anonymity extends from location k-anonymity that has been widely used to address this issue. The main challenge of trajectory k-anonymity is the selection of a trajectory k-anonymity set. However, existing anonymity methods ignore the trajectory similarity and direction, assuming that it has little impact on privacy. Thus, it cannot provide a preferable trajectory k-anonymity set. In this paper, we propose to use trajectory angle to evaluate trajectory similarity and direction, and construct an anonymity region on the basis of trajectory distance. Considering the various preference settings on the proportion of trajectory privacy and data utility in different scenarios, we propose a personalized anonymization model to select the trajectory k-anonymity set. Experiment results prove that our method can provide an effective trajectory k-anonymity set under various proportions of trajectory privacy and data utility requirements, while the efficiency just reduces a little. (C) 2013 Elsevier Ltd. All rights reserved.