An Investigation of Data Privacy and Utility Preservation using KNN Classification as a Gauge
This addresses data privacy issues for organizations handling personal identifiable information, but it appears incremental as it uses an existing method (KNN) on new data without novel solutions.
The study tackled the challenge of balancing data privacy and utility, which is NP-hard, by using KNN classification as a gauge to measure the trade-off, but no concrete results or numbers were provided.
It is obligatory that organizations by law safeguard the privacy of individuals when handling data sets containing personal identifiable information (PII). Nevertheless, during the process of data privatization, the utility or usefulness of the privatized data diminishes. Yet achieving the optimal balance between data privacy and utility needs has been documented as an NP-hard challenge. In this study, we investigate data privacy and utility preservation using KNN machine learning classification as a gauge.