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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As an integral part of data security, identity disclosureis a major privacy breach, which reveals the identification of entities with certain background knowledge known by an adversary. Most recent studies on this problem focus on the protection of relational data or simple graph data (i.e. undirected, un weighted and acyclic). However, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. As more real-world graphs or social networks are released publicly, there is growing concern about privacy breaching for the entities involved. In this paper, we first formalize a general anonymizing model to deal with weight-related attacks, and discuss an efficient metric to quantify information loss incurred in the perturbation. Then we consider a very practical attack based on the sum of adjacent weights for each vertex, which is known as volume in graph theory field. We also propose a complete solution for the weight anonymization problem to prevent a graph from volume attack. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.