Clustering-Based K-Anonymisation Algorithms

Abstract. K-anonymisation is an approach to protecting private information contained within a dataset. Many k-anonymisation methods have been proposed recently and one class of such methods are clusteringbased. These methods are able to achieve high quality anonymisations and thus have a great application potential. However, existing clusteringbased techniques use different quality measures and employ different data grouping strategies, and their comparative quality and performance are unclear. In this paper, we present and experimentally evaluate a family of clustering-based k-anonymisation algorithms in terms of data utility, privacy protection and processing efficiency.

• Type: Conference
• Year: 2007
• Where: DEXA
• Authors: Grigorios Loukides, Jianhua Shao
• Keywords: Data Grouping Strategies, DEXA 2007, Different Quality Measures, Many K-anonymisation Methods