查看文章

A voluminous amount of data regarding users’ location services is being generated and shared every second. The anonymization plays a major role in data sanitization before sharing it to the third party by removing directly linked personal identifiers of an individual. However, the rest of the non-unique attributes, i.e., quasi-identifiers (QIDs) can be used to identify unique identities in a dataset or linked with other dataset attributes to infer the identity of users. These attributes can lead to major information leakage and also generate threat to user data privacy and security. So, the selection of QID from users’ data acts as a first step to provide individual data privacy. This paper provides an understanding of the quasi-identifier and discusses the importance to select QID efficiently. The paper also presents the different methods to select quasi-identifier efficiently in order to provide privacy that eliminates re-identification …