Abstract The rapid growth in the usage of location-based services has resulted in extensive research on users’ trajectory data publishing. But, a key concern here is a potential breach of user privacy through various linkage attacks by an efficient adversary. There exist a few privacy preservation methods to defend against either single or combination of linkage attacks, namely Identity linkage attack, Attribute linkage attack, and Similarity attack. However, the Correlated-records linkage attack has not been studied in any previous privacy preservation methods, and there is no privacy preservation method to address all the above four linkage attacks. In this paper, a novel anonymization method is proposed to provide the privacy guarantee to users against all the four linkage attacks. The proposed method consists of two phases, namely virtualization and suppression. The virtualization method works as a replacement mechanism for the sensitive attribute and the suppression method works as anonymization mechanism for users trajectories, in order to anonymize the trajectory datasets for preserving users’ privacy from the above four linkage attacks. To validate the efficiency of the proposed method, it is also compared with existing methods, namely KCL-L, KCL-G and KCL-PPTD, considering both synthetic and real-time datasets. The experimental results exhibit that the proposed approach results in better performance with a significant reduction in the information loss when compared to other states of the art methods.

中文翻译：

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先验知识对轨迹数据发布隐私泄露的影响

摘要 位置服务使用的快速增长导致了对用户轨迹数据发布的广泛研究。但是，这里的一个关键问题是高效对手通过各种链接攻击可能会侵犯用户隐私。存在几种隐私保护方法来防御单一或组合的链接攻击，即身份链接攻击、属性链接攻击和相似性攻击。然而，以往的任何隐私保护方法都没有研究过关联记录联动攻击，也没有任何隐私保护方法可以解决上述四种联动攻击。在本文中，提出了一种新颖的匿名化方法，为用户提供针对所有四种链接攻击的隐私保证。所提出的方法由两个阶段组成，即虚拟化和抑制。虚拟化方法作为敏感属性的替换机制，抑制方法作为用户轨迹的匿名化机制，以匿名化轨迹数据集，保护用户隐私免受上述四种联动攻击。为了验证所提出方法的效率，还考虑了合成数据集和实时数据集，将其与现有方法（即 KCL-L、KCL-G 和 KCL-PPTD）进行了比较。实验结果表明，与其他现有技术方法相比，所提出的方法具有更好的性能，同时显着减少了信息丢失。虚拟化方法作为敏感属性的替换机制，抑制方法作为用户轨迹的匿名化机制，以匿名化轨迹数据集，保护用户隐私免受上述四种联动攻击。为了验证所提出方法的效率，还考虑了合成数据集和实时数据集，将其与现有方法（即 KCL-L、KCL-G 和 KCL-PPTD）进行了比较。实验结果表明，与其他现有技术方法相比，所提出的方法具有更好的性能，同时显着减少了信息丢失。虚拟化方法作为敏感属性的替换机制，抑制方法作为用户轨迹的匿名化机制，以匿名化轨迹数据集，保护用户隐私免受上述四种联动攻击。为了验证所提出方法的效率，还考虑了合成数据集和实时数据集，将其与现有方法（即 KCL-L、KCL-G 和 KCL-PPTD）进行了比较。实验结果表明，与其他现有技术方法相比，所提出的方法具有更好的性能，同时显着减少了信息丢失。它还与现有的方法，即 KCL-L、KCL-G 和 KCL-PPTD 进行了比较，同时考虑了合成和实时数据集。实验结果表明，与其他现有技术方法相比，所提出的方法具有更好的性能，同时显着减少了信息丢失。它还与现有的方法，即 KCL-L、KCL-G 和 KCL-PPTD 进行了比较，同时考虑了合成和实时数据集。实验结果表明，与其他现有技术方法相比，所提出的方法具有更好的性能，同时显着减少了信息丢失。