  Data controllers manage immense data, and occasionally, it is released
publically to help the researchers to conduct their studies. However, this
publically shared data may hold personally identifiable information (PII) that
can be collected to re-identify a person. Therefore, an effective anonymization
mechanism is required to anonymize such data before it is released publically.
Microaggregation is one of the Statistical Disclosure Control (SDC) methods
that are widely used by many researchers. This method adapts the k-anonymity
principle to generate k-indistinguishable records in the same clusters to
preserve the privacy of the individuals. However, in these methods, the size of
the clusters is fixed (i.e., k records), and the clusters generated through
these methods may hold non-homogeneous records. By considering these issues, we
propose an adaptive size clustering technique that aggregates homogeneous
records in similar clusters, and the size of the clusters is determined after
the semantic analysis of the records. To achieve this, we extend the MDAV
microaggregation algorithm to semantically analyze the unstructured records by
relying on the taxonomic databases (i.e., WordNet), and then aggregating them
in homogeneous clusters. Furthermore, we propose a distance measure that
determines the extent to which the records differ from each other, and based on
this, homogeneous adaptive clusters are constructed. In experiments, we
measured the cohesiveness of the clusters in order to gauge the homogeneity of
records. In addition, a method is proposed to measure information loss caused
by the redaction method. In experiments, the results show that the proposed
mechanism outperforms the existing state-of-the-art solutions.
