Title :
B-CASTLE: An Efficient Publishing Algorithm for K-Anonymizing Data Streams
Author :
Wang, Pu ; Lu, Jianjiang ; Zhao, Lei ; Yang, Jiwen
Author_Institution :
Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China
Abstract :
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.
Keywords :
data privacy; pattern clustering; publishing; B-CASTLE; information loss; k-anonymizing data stream; pattern clustering; privacy preserving publishing algorithm; publishing algorithm; Algorithm design and analysis; Clustering algorithms; Correlation; Data privacy; Merging; Privacy; Publishing; CASTLE; balanced publishing; data streams; privacy preserving;
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
DOI :
10.1109/GCIS.2010.196