CASTLE is a popular privacy preserving publishing algorithm for k-anonymizing data streams. However, the algorithm does not consider the influence of the distribution of a data stream for clustering, and besides, it has a higher information loss when re-clustering and publishing all the tuples simultaneously. Therefore, in this paper, we propose a novel algorithm of B-CASTLE to solve these deficiencies. B-CASTLE adjusts the tuples into clusters dynamically when clustering, and merges only part of the relevant clusters at a time when publishing. Experiments show that B-CASTLE has a better performance than CASTLE on privacy preservation and efficiency. © 2010 IEEE.
CITATION STYLE
Wang, P., Lu, J., Zhao, L., & Yang, J. (2010). B-CASTLE: An efficient publishing algorithm for k-anonymizing data streams. In Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010 (Vol. 2, pp. 132–136). https://doi.org/10.1109/GCIS.2010.196
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