Name
Abstract | ||
|---|---|---|
K-anonymity is one simple and efficient method to achieve sensitive data protected in data sharing application. The traditional k-anonymity techniques, however, have all tuples of publishing database involve in anonymity generalize which lead to reduce the precision of publishing table. This paper firstly proposes a Naïve Sensitive Tuple Anonymity Method. In this method only sensitive tuples are generalized, and the other tuples can be directly published. In order to improve the security of publish data, after the sensitive tuples were anonymized, we selected some proportion non-sensitive tuples anonymized and imported into corresponding sensitive tuple anonymity group, which can guarantee the density of the sensitive tuples satisfied with equal to 1/m in every anonymity group. Experiment results on the Adult Database show the proposed methods not only can improve the accuracy of the releasing data, but also can preserve privacy. The algorithm is effective and efficient. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/DBTA.2009.74 | DBTA |
Keywords | Field | DocType |
publishing,sun,database management systems,data protection,satisfiability,data security,computer science,security,data privacy,application software,databases,privacy,cancer | Data mining,Data security,Tuple,Computer science,Data sharing,k-anonymity,Anonymity,Information privacy,Application software | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinping Hu | 1 | 0 | 0.68 |
| Zhihui Sun | 2 | 3 | 0.76 |
| Yingjie Wu | 3 | 40 | 12.68 |
| Wenyu Hu | 4 | 2 | 1.39 |
| Jiancheng Dong | 5 | 2 | 3.76 |