Achieving (α, k)-anonymity via clustering in data publishing

作者：Yang, Gao Ming^*; Yang, Jing; Zhang, Jian Pei

来源：Acta Electronica Sinica, 2011, 39(8): 1941-1946.

• Clustering
• Data publishing
• K-Anonymity
• L diversities
• Privacy preserving

To better protect personal privacy against background knowledge attack and homogeneity attack, single sensitive value and multi sensitive values (α, k)-anonymity models were defined respectively. For achieving this purpose, two clustering algorithms were designed. At the same times, we made correctness and complexity analysis for the algorithms. Since the data sets contain continuous attributes and classification attributes, a detailed mapping and processing method was given, that make the distance between data points can calculate easily, and avoid completely the case that confusion data points distance and information loss. Experiment results and detailed theory analysis demonstrate that our methods are effective on both information loss and execution time comparing with existing methods.

• 出版日期2011
• 单位哈尔滨工程大学