2015 | OriginalPaper | Buchkapitel
An Efficient Local-Recoding k-Anonymization Algorithm Based on Clusterin
Erschienen in:
Transactions on Edutainment XI
Abstract
KACA is a typical local-recoding k-anonymization algorithm. It can generate k-anonymizing data with high quality. The main drawback of KACA algorithm is its high computational cost in dealing with large dataset. To remedy this problem, we propose an new efficient k-anonymization algorithm. The main idea of the proposed algorithm is that we first adopt the c-modes algorithm to partition the whole dataset into some large clusters, and then take KACA algorithm to k-anonymize each cluster separately. Finally, comprehensive experiments demonstrate the effectiveness of our algorithm.
