Paper Title:
Increment Update Algorithms Basing on Semantic Similarity Degree for K-Anonymized Dataset
  Abstract

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, J. W. Liu, Y. Qian, X. S. Liu, J. L. Song, "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
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Xiao Hong Liao, Ping Hua Chen
Chapter 2: Advanced Сomputer and Information Technology, Applications Simulation, Measurements and Electronics Engineering
Abstract:Due to the simplicity and high recommending quality, collaborative filtering algorithms are the most successful recommender techniques and...
247
Authors: Zhen Zhong Jin, Zheng Huang, Hua Zhang
Chapter 9: Applied and Computational Mathematics, Methods and Algorithms Optimization and Data Processing
Abstract:The suffix tree is a useful data structure constructed for indexing strings. However, when it comes to large datasets of discrete contents,...
867