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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Privacy-Preserving on Graphs Using Randomization and Edge-Relevance
verfasst von : Jordi Casas-Roma
Erschienen in: Modeling Decisions for Artificial Intelligence
Verlag: Springer International Publishing
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The problem of anonymization on graphs and the utility of the released data are considered in this paper. Although there are some anonymization methods for graphs, most of them cannot be applied on medium or large networks due to their complexity. Nevertheless, random-based methods are able to work with medium or large networks while fulfilling the desired privacy level. In this paper, we devise a simple and efficient algorithm for randomization on graphs. Our algorithm considers the edge’s relevance, preserving the most important edges of the graph, in order to improve the data utility and reduce the information loss on anonymous data. We apply our algorithm to different real datasets and demonstrate their efficiency and practical utility.