Name
Abstract | ||
|---|---|---|
With the increasing popularity of online social networks, such as twitter and weibo, privacy preserving publishing of social network data has raised serious concerns. In this paper, we focus on the problem of preserving the sensitive edges in social network data. We call a graph is k-sensitive anonymous if the probability of an attacker can re-identify a sensitive node or a sensitive edge is at most $\frac{1}{k}$. To achieve this objective, we devise two efficient heuristic algorithms to respectively group sensitive nodes and create non-sensitive edges. Finally, we verify the effectiveness of the algorithm through experiments. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-38562-9_57 | WAIM |
Keywords | Field | DocType |
sensitive edge,efficient heuristic algorithm,non-sensitive edge,sensitive node,sensitive edges protection,social network data,online social network,group sensitive node,increasing popularity,serious concern | Graph,Data mining,Heuristic,Social network,Computer science,Popularity,Data publishing,Publishing | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liangwen Yu | 1 | 7 | 3.49 |
| Tao Yang | 2 | 10 | 5.65 |
| Zhengang Wu | 3 | 1 | 3.06 |
| Jiawei Zhu | 4 | 16 | 7.17 |
| Jianbin Hu | 5 | 179 | 22.43 |
| Zhong Chen | 6 | 503 | 58.35 |