Name
Abstract | ||
|---|---|---|
K-anonymity is a fine approach to protecting privacy in the release of microdata for data mining. Microaggregation and generalization are two typical methods to implement k-anonymity. But both of them have some defects on anonymizing mixed microdata. To address the problem, we propose a novel anonymization method, named MAGE, which can retain more semantics than generalization and microaggregation in dealing with mixed microdata. The idea of MAGE is to combine the mean vector of numerical data with the generalization values of categorical data as a clustering centroid and to use it as incarnation of the tuples in the corresponding cluster. We also propose an efficient TSCKA algorithm to anonymize mixed data. Experimental results show that MAGE can anonymize mixed microdata effectively and the TSCKA algorithm can achieve better trade-off between data quality and algorithm efficiency comparing with two well-known anonymization algorithms, Incognito and KACA. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.knosys.2013.10.009 | Knowl.-Based Syst. |
Keywords | Field | DocType |
k-anonymization method,generalization value,data mining,numerical data,tscka algorithm,categorical data,algorithm efficiency,data quality,efficient tscka algorithm,mixed data,mixed microdata,generalization | Data mining,Algorithmic efficiency,Data quality,Categorical variable,Tuple,Computer science,k-anonymity,Microdata (HTML),Cluster analysis,Centroid | Journal |
Volume | ISSN | Citations |
55, | 0950-7051 | 5 |
PageRank | References | Authors |
0.40 | 29 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianmin Han | 1 | 25 | 5.74 |
| Juan Yu | 2 | 8 | 3.15 |
| Yuchang Mo | 3 | 123 | 10.63 |
| Jianfeng Lu | 4 | 26 | 7.61 |
| Huawen Liu | 5 | 468 | 27.18 |