A generalization based approach for anonymizing weighted social network graphs
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Sensitive label privacy protection on social network data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Privacy Preservation by k-Anonymization of Weighted Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph by having the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attack as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.