A framework for privacy-preserving cluster analysis

Abstract--Releasing person-specific data could potentially reveal sensitive information of individuals. k-anonymization is a promising privacy protection mechanism in data publishing. Though substantial research has been conducted on kanonymization and its extensions in recent years, few of them consider releasing data for a specific purpose of data analysis. This paper presents a practical data publishing framework for determining a generalized version of data that preserves both individual privacy and information usefulness for cluster analysis. Experiments on real-life data suggest that, by focusing on preserving cluster structure in the generalization process, the cluster quality is significantly better than the cluster quality on the generalized data without such focus. The major challenge of generalizing data for cluster analysis is the lack of class labels that could be used to guide the generalization process. Our approach converts the problem into the counterpart problem for c...

• Type: Journal
• Year: 2008
• Where: ISI
• Authors: Benjamin C. M. Fung, Ke Wang, Lingyu Wang, Mourad Debbabi
• Keywords: Cluster Analysis, Cluster Quality, Cluster Structure, ISI 2008, Security Privacy