面向多敏感属性医疗数据发布的隐私保护技术

针对目前多敏感属性医疗数据发布问题，在分析多维桶分组技术的基础上，继承了有损连接对隐私数据进行保护的思想，提出了一种基于相同敏感属性集的L-覆盖性聚类分组方法。首先计算每条记录的相同敏感属性集，然后按照聚类的思想将满足L-覆盖性的记录进行分组。同时给出了L-覆盖性聚类分组的实现算法(LCCU)。实际数据集上的大量实验结果表明，该方法可以有效防止隐私泄露，同时增强数据的可用性。

Privacy Preserving Technology for Multiple Sensitive Attributes in Medical Data Publishing

In view of the privacy leak problem of secure data publishing when sensitive data contains multi-attributes,on the basis of analysing the multi dimension bucket approach,this paper proposed an 1-coverage clustering grouping approach based on the same sensitive attribute set and the idea of lossy join. Firstly it calculated the same sensitive attribute set of each record, and then we grouped each record which satisfies the constraints of L-coverage following the idea of clustering. Also we designed a LCCG algorithm to implement the approach. Experimental results on the real world datasets show that the new model is able to reduce privacy disclosure apparently and enforce security of data publishing.