Label-bag based Graph Anonymization via Edge Addition

Abstract

Privacy-preserving publishing of graph data, such as social networks, has been gaining much public attention in recent years due to the growing demands for publishing graph data containing privacy information. Most of the existing approaches for graph anonymization deal with unlabeled graphs, while labeled graphs have useful real-life applications. However, it is proven that k-anonymity problem edgelabeled graphs is computationally expensive. In this paper, we devise a greedy heuristic based approach for k-anonimity problem over edge-labeled graphs. More precisely, we deal with some utility metrics to achieve better anonymization results. To show the effectiveness of the proposed schemes, we conduct a set of experimental evaluations using synthetic and real datasets. The results reveal that our proposed scheme can successfully anonymize edge-labeled graphs. We also assess how the utility of the anonymized graphs is affected by the proposed algorithms.

Anonymization and De-anonymization of Social Network Data

“…We have also seen some very specific k-anonymity definitions surface. For example, recently in (Li et al 2014), they build on the work of Yuan in (Yuan et al 2010) in which they considered personalized privacy protection for anonymizing graph data in terms of both semantic and structural information. Based on the adversary's semantic and structural background knowledge, they customized three levels of privacy protection.…”