Evaluating data utility of privacy-preserving pseudonymized location datasets
Evaluating data utility of privacy-preserving pseudonymized location datasets
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DOI：
10.22667/jowua.2014.09.31.063
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Tomoya Tanjo;Kazuhiro Minami;K. Mano;H. Maruyama
中科院分区：
文献类型：
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作者：
Tomoya Tanjo;Kazuhiro Minami;K. Mano;H. Maruyama
假名化是以保护隐私的方式发布包含轨迹信息的位置数据集的有效方法。我们之前提出了一种在用户同时相遇的混合区域随机交换多个用户的假名的技术，以防止对手重新识别目标用户的多个轨迹段。然而，这种分割技术本质上将用户的整个轨迹路径分成多个段，从而降低了数据集的实用性。因此，在本文中，我们通过使用真实位置数据集进行各种实验来评估数据效用和隐私之间的权衡。我们的实验结果表明，在满足现实隐私要求的同时，可以获得足够的数据效用。
Pseudonymization is an effective way to publish a location dataset with trajectory information in a privacy-preserving way. We previously proposed a technique of randomly exchanging multiple users’ pseudonyms at a mix zone where the users meet at the same time to prevent an adversary from reidentifying multiple trajectory segments of a target user. However, such a segmentation technique essentially divides a user’s whole trajectory path into multiple segments and thus degrades the utility of the dataset. In this paper, we, therefore, evaluate tradeoffs between data utility and privacy by conducting various experiments with a real location dataset. Our experimental results show that it is possible to achieve sufficient data utility while satisfying realistic privacy requirements.