Approaches for Preserving FDs in it K-anonymization

摘要：

K-anonymization essentially is some update operations over the original dataset. So, to guarantee the integrity of the dataset, its necessary to preserve the functional dependencies (FDs) in k-anonymization. We present several approaches to maintain FDs in k-anonymization. One is detecting FDs violation constantly while Aanonymizing, which can be merged to numerous previous k-anonymized algorithms. Another is based on clusters combination, which is suit for k-anonymized algorithms using clustering or microaggregation. The third is a more directly and valid approach based on K-MSD and associated generalization, which focuses on preserving FDs as well as higher data precision and increases the utility of the anonymized dataset effectively.

关键词： k-anonymity FDs FDs violation clusters combination,K-MSD associated generalization

作者: Jinling Song Guangbin Zhang Liming Huang Xingshun Liu Danli Wang

作者单位: Department of Computer Yanshan University Qinhuangdao 066004,P. R. China HeBei Normal University of HeBei Normal University of Science & Tehnology Qinhuangdao 066004,P. R. China HeBei Normal University of Science & Technology Qinhuangdao 066004,P. R. China Department of Computer Yanshan University Qinhuangdao 066004,P. R. China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

会议地点: 长春

会议语种:英文

页码: 334-337

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）