Location Data Anonymization Retaining Data Mining Utilization
Location Data Anonymization Retaining Data Mining Utilization
复制标题
位置数据匿名化保留数据挖掘利用
DOI：
10.1007/978-3-031-22137-8_30
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Morimoto Yasuhiko
中科院分区：
文献类型：
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作者：
Iwata Naoto;Kamei Sayaka;Alam Kazi Md. Rokibul;Morimoto Yasuhiko
位置信息，如客户的家庭地址，对于数据挖掘任务是必不可少的。另一方面，它是敏感的私人信息。在大多数国家，当请求第三方进行数据挖掘时，法律要求它在数据库中匿名个人信息，如家庭地址。然而，传统的匿名化方法大大失去了位置信息固有的有用性。本文提出了一种保留重要位置特征的位置匿名化方法。在所提出的方法中，将每个地址替换为到每个设施的距离的排名值，该距离在位置方面是重要的，例如车站或超市。我们检查了我们的方法，并确认从非匿名数据中挖掘的重要规则也可以从我们的匿名数据中挖掘出来。
Location information, such as customers’ home addresses, is essential for data mining tasks. On the other hand, it is sensitive private information. In most countries, when requesting a third party for data mining, it is legally required to anonymize personal information such as home addresses in the database. However, conventional anonymization methods significantly lose the usefulness that location information has inherently. In this paper, we proposed an anonymization method of location retaining important locational features. In the proposed method, each address is replaced with a ranking value of the distance from each facility that is important in terms of location, such as a station or a supermarket. We examined our method and confirmed that the important rules mined from non-anonymized data could also be mined from our anonymized data.