Increment Update Algorithms Basing on Semantic Similarity Degree for K-Anonymized Dataset

Abstract:

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To keep the k-anonymized dataset consistent with the original dataset in real time, the increment update algorithms basing on Semantic Similarity Degree for the k-anonymized dataset are presented. For each update operation on original dataset, the position of the tuple to be updated is located firstly on k-anonymized dataset by Semantic Similarity Degree and then the corresponding update operation is processed. The increment update algorithms not only guarantee k-anonymized dataset updating with original dataset simultaneously, but also avoid big changes in k-anonymized dataset.

Info:

Periodical:

Edited by:

Yanwen Wu

Pages:

328-333

DOI:

10.4028/www.scientific.net/AMR.267.328

Citation:

L. M. Huang et al., "Increment Update Algorithms Basing on Semantic Similarity Degree for K-Anonymized Dataset", Advanced Materials Research, Vol. 267, pp. 328-333, 2011

Online since:

June 2011

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Price:

$35.00

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