In the process of clustering

inappropriate distance measure leads to unnecessary loss of information during the anonymous process

so it is a difficult problem to define a proper distance measurement for different types of variables.We put forward the concept of semantic attribute

and propose a coding hierarchy tree to represent semantic attribute and to reduce the information loss in the anonymous process.In the

p

-sensitive

k

-anonymity model

the uneven distribution of the sensitive attribute values in the clustering results may cause sensitive information disclosure

so we propose a micro-aggregation algorithm based on sensitive attribute entropy.Moreover

we propose the concept of anonymous protection factor to descr

ibe the degree of privacy protection.During the process of clustering

in order to improve the uniformity of the distribution of sensitive attribute values in the clustering results

the algorithm ensures the maximum of anonymous protection factor

so it can deal with the background knowledge attack and reduce the risk of privacy leaking.Finally

the rationality and validity of the algorithm is verified by experiment.