Data Quality in Privacy Preservation for Associative Classification

Privacy preserving has become an essential process for any data mining task. Ill general, data transformation is needed to ensure. privacy preservation. Once the privacy is preserved, data quality issue must be addressed, i.e. the impact oil data quality should be minimized. In this papers k-Anonymization is considered as the transformation approach for preserving data privacy. In such a context, we discuss the metrics of the data quality in terms of classification, which is one of the most important tasks in data mining. Since different type of classification may use different approach to deliver knowledge, data quality metric for the classification task should be tailored to a certain type of classification. Specifically: we propose a frequency-based data. quality metric to represent the data quality of the transformed dataset, in the situation that associative classification is to be processed. Subsequently, we validate our proposed metric with experiments. The experiment results have shown that our proposed metric can effectively reflect the dual quality for the associative classification problem.