Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-Closeness.A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice.

 

Many studies regarding anonymized databases of personal information have been proposed. Most existing methods consider that the data holder has a database in the form of explicit identifiers, quasi-identifiers (QIDs), or sensitive attributes, where explicit identifiers are attributes that explicitly identify individuals (e.g., name), QIDs are attributes that could be potentially combined with other directories to identify individuals (e.g., zip code and age), and sensitive attributes are personal attributes of a private nature (e.g., disease and salary)

Anonymization of Sensitive Quasi-Identifiers.A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice.