Abstract: K-anonymity is an effective method to prevent linking attack and protect privacy. The main idea of k-anonymity is generalizing the values on a set of special attributes named ctuasi-identifier, so that gains a k-anonymized dataset in which the values of each tuple on quasi-identifier must repeat at least k occurrences. Although k-anonymized dataset guarantees privacy, the k-anonymized dataset needs to be updated constantly because the original dataset updates occasionally after a version of k-anonymized dataset has been existed. So, how to update the k-anonymized dataset as well as the original dataset becomes an urgent problem. To solve this problem, based on the detailed analysis to various update situations of the k-anonymized dataset, the increment update algorithms for the k-anonymized dataset were presented.

Key words: k-anonymity, Increment update, Insert, Delete, Modify