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K-anonymity is an effective method to prevent linking attacks and protect privacy. Although the k-anonymous dataset guarantees privacy, it must be constantly updated because the original dataset updates occasionally after a version of k-anonymous dataset has been exposed. So, how to update the k-anonymous dataset simultaneously when the original dataset has been updated becomes an urgent problem. To solve this problem, according to the mapping relation between tuples of the original dataset and k-anonymous dataset, a kind of tree structure similar to a B-tree is proposed, so that the update operations on the original dataset can be converted into the corresponding operations of leaf node in the similar-B-tree. Based on this, an incremental updating method for the k-anonymous dataset using a similar-B-tree is presented. This method can reflect the changes in the original dataset to the k-anonymous dataset in time, and it can greatly improve the update efficiency of the k-anonymous dataset.
Incremental update; k-anonymous dataset; similar-B-tree; spatial point
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