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 clusteringbased k-anonymization method that runs in O( , the experiments show that our method outperforms their method with respect to information loss and resilience to outliers.

No abstract

No abstract