Multivariate Microaggregation of Set-Valued Data

Abstract

Data controllers manage immense data, and occasionally, it is released publically to help the researchers toconduct 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 toanonymize 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 togenerate 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 clusteringtechnique that aggregates homogeneous records in similar clusters, and the size of the clusters is determinedafter the semantic analysis of the records. To achieve this, we extend the MDAV microaggregation algorithm tosemantically analyze the unstructured records by relying on the taxonomic databases (i.e., WordNet), and thenaggregating them in homogeneous clusters. Furthermore, we propose a distance measure that determines theextent 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 ofrecords. 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.