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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...
Compared with generalization, anatomy preserves both the privacy and the correlation in data publication. On the other hand, data streams have gradually become a widely used data representation. Therefore, in this paper, we develop a novel algorithm of SANATOMY, to solve the problem of anatomized publishing of data streams. It creates l-diverse buckets according to the stream tuples' sensitive values,...
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