Name
Abstract | ||
|---|---|---|
With the unstoppable growth of applications requiring data to be represented as graphs, the interest for keeping this type of data private also grows. While many efforts have been made in order to anonymize tabular data, anonymizing graphs is a recent topic of research. Previous work on graph anonymization assumes that techniques proposed for tabular data are not suitable for anonymizing graph-like data because these ignore the topological properties of the graph. Because of this, they resort to new graph anonymization techniques that require devising new complex algorithms. In this paper, we show that, contrarily to this well-established assumption, it is possible to use classical anonymization techniques for graph anonymization. For this, we propose to embed the graph into a multidimensional vector space that approximately preserves the distances between any two vertices in the graph. This way, the graph can be represented as a list of vectors on which we can use tabular anonymization techniques. We show that, with our proposal, we can successfully anonymize graphs directly using the most common tabular techniques. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/IDA-140646 | Intell. Data Anal. |
Keywords | Field | DocType |
data privacy,graph anonymization,metric embedding,tabular data anonymization | Data mining,Graph,Vector space,Vertex (geometry),Computer science,Theoretical computer science,Information privacy,Graph (abstract data type) | Journal |
Volume | Issue | ISSN |
18 | 3 | 1088-467X |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnau Padrol | 1 | 32 | 7.93 |
| Victor Muntés-Mulero | 2 | 204 | 22.79 |