Name
Abstract | ||
|---|---|---|
Existing sensitive attributes diversity models do not capture the semantic similarity between sensitive values, so they cannot resist semantic similarity attack. To address the problem, we present a method to measure semantic similarity of a categorical sensitive attribute based on the attribute' semantic hierarchy tree. On basis of the measurement, the paper proposes a (l, e)-diversity model which has two constraints in each equivalence class: (1) there are at least l well-represented values; (2) any two sensitive values are not e-similar. Furthermore, the paper designs a liner-complexity maximum bucketization greedy algorithm to implement the model. Experimental results show that the anonymous data satisfied (l, e)-diversity has a higher diversity degree than that satisfied l-diversity, so (l, e)-diversity can protect privacy more effectively than l-diversity. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4304/jcp.9.1.59-64 | JOURNAL OF COMPUTERS |
Keywords | Field | DocType |
Data privacy, l-diversity, (l, e)-diversity, anatomy, semantic similarity attack | Semantic similarity,Data mining,Categorical variable,Semantic hierarchy,Computer science,Resist,Greedy algorithm,Equivalence class,Information privacy | Journal |
Volume | Issue | ISSN |
9 | 1 | 1796-203X |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyuan Wang | 1 | 0 | 1.35 |
| Jianmin Han | 2 | 36 | 8.16 |
| Ji-yi Wang | 3 | 17 | 8.05 |
| Lixia Wang | 4 | 0 | 0.34 |