 |
The proposed binary tree-based clustering algorithm BTCA is compared with the k-anonymous classical algorithm KACA algorithm to measure the sum of information loss. Considering that the number of quasi-identifiers also affects the amount of information loss, the number of quasi-identifiers is set to eight for experiments, including four numerical attributes {age, education-num, capital-gain, capital-loss}, and four subtype attributes {workclass, sex, occupation, education}. The experimental results are shown in Figure . It can be seen that under the increasing K value, the BTCA algorithm proposed has less information loss, and the advantage is more obvious and the data availability is higher with the increase of K value.
|
IEEE/ICACT20230212 Slide.15
[Big Slide]