A Method of Data Anonymization Based on Significant Degree of Quasi Attributes and Density Based Clustering
Qiu Tao-ron · Journal of Nanchang University · 2013
Some non-rational clusters was easily generated by Maximum Distance to Average Vector(MDAV) without considering the data distribution and the significance of the quasi-attributes in clustering,which may bring the influence of the trade-off between the utility of anonymized data and privacy protection.In order to deal with the problem,a MDAV algorithm was proposed for modifying of k-anonymity of data.In the proposed method,the clusters were generated based on the density method DENCLUE on the given data.And the weighted distance measure,in which the weighted values were obtained by using the quasi attributes significances computed based on Rough set method,was used to implement k-partition in each cluster.Experimental results showed that the k-anonymity based on the proposed MDAV modified method can generate anonymity table improving the utility of anonymized data.