Association for Computing Machinery
The authors study the problem of anonymizing data with quasi-sensitive attributes. Quasi-sensitive attributes are not sensitive by themselves, but certain values or their combinations may be linked to external knowledge to reveal indirect sensitive information of an individual. They formalize the notion of l-diversity and t-closeness for quasi-sensitive attributes, which they call QS l-diversity and QS t-closeness, to prevent indirect sensitive attribute disclosure. They propose a two-phase anonymization algorithm that combines quasi-identifying value generalization and quasi-sensitive value suppression to achieve QS l-diversity and QS t-closeness.