**3.3 Homogeneity attacks**

In cases where all or most of the sensitive attributes in the groups included in the anonymous tables are similar, the privacy of data owners is at risk of violation.

In order to prevent homogeneity attacks, it is necessary to prevent similar sensitive attributes within the groups in the anonymous table from being in the same group or to reproduce heterogeneous records by diluting the homogeneous attributes with the record duplication approach [34].

### **3.4 Skewness attacks**

The statistical distribution of sensitive attribute values in published or shared anonymous data sets can lead to the success of skewness attacks against privacy. The distortion in the general distribution of sensitive attributes occurs when these values are too dominant and anonymous data sets become vulnerable [35].
