On Enhancing Utility in k-Anonymization
The k-anonymity is one of the most studied models of privacy preserving technology. It limits the linking confidence between specific sensitive information and a specific individual by hiding the identifications of each individual into at least k-1 others in the database. A k-anonymization algorithm is usually evaluated using information loss or data utility metrics. In this paper, the authors first propose a new quality metric, called the Efficiency metric. This metric overcomes the limitations of existing one dimensional metrics, representing either privacy measure or data utility measure, used in privacy preserving data sharing.