医疗数据发布中属性顺序敏感的隐私保护方法

隐私保护已成为包含微数据应用诸如医疗数据发布共享或数据挖掘中的一个重要问题.基于全局重编码或局部重编码的匿名性方法,通过保证每一条数据记录都至少有某个数量的其他记录与其具有同样的特征来保护隐私性.如果考虑到对处理后的数据进行属性顺序敏感的数据分析任务,这类方法并不能很好地完成任务.研究基于数据可用性指标的匿名性方法,着重考虑数据分析任务中的属性顺序对于匿名性方法的影响.从多维数据匿名的概念出发,讨论用于该类情况下的数据匿名性方法.在公开数据集上的实验结果表明,该方法对于上述问题是有效的,并且效率并未受到影响.

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. This paper studies 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.