Privacy Preservation for Attribute Order Sensitive Workload in Medical Data Publishing
Privacy becomes a more serious concern in applications involving microdata such as medical data publishing or medical data mining. Anonymization methods based on global recoding or local recoding or clustering provide privacy protection by guaranteeing that each released record will be indistinguishable to some other individual. However, such methods may not always achieve effective anonymization in terms of analysis workload using the anonymized data. The utility of attributes has not been well considered in the previous methods. In this paper, we study the problem of utility-based anonymization to concentrate on attributes order sensitive workload, where the order of the attributes is important to the analysis workload. Based on the multidimensional anonymization concept, a method is discussed for attributes order sensitive utility-based anonymization. The performance study using public data sets shows that the efficiency is not affected by the attributes order processing.
GAO Ai-qiang DIAO Lu-hong
Beijing Electric Power Company, Beijing, 100031, China College of Applied Sciencs, Beijing University of Technology, Beijing, 100124, China
国际会议
2009 IEEE International Symposium on IT in Medicine & Education( IEEE 教育与医药信息化国际会议)
济南
英文
1140-1145
2009-08-14（万方平台首次上网日期，不代表论文的发表时间）