An efficient clustering method for k-anonymization

Abstract

The k-anonymity model is a privacy-preserving approach that has been extensively studied for the past few years. To minimize the information loss due to anonymization, it is crucial to group similar data together and then anonymize each group individually. This work proposes a clustering-based k-anonymization method that runs in O(n²/k) time. We experimentally compare our method with another clustering-based k-anonymization method recently proposed by Byun et al. Even though their method has a time complexity of O(n²), the experiments show that our method outperforms their method with respect to information loss and resilience to outliers.