Increment Update Algorithms Basing on Semantic Similarity Degree for K-Anonymized Dataset
Li Ming Huang, Jing Wen Liu, Ying Qian, Xing Shun Liu, Jin Ling Song · Advanced materials research · 2011
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.