Abstract-Publishing social network data for research purposes has raised serious concerns for individual privacy. There exist many privacy-preserving works that can deal with different attack models. In this paper, we introduce a novel privacy attack model and refer it as a mutual friend attack. In this model, the adversary can re-identify a pair of friends by using their number of mutual friends. To address this issue, we propose a new anonymity concept, called k-NMF anonymity, i.e., k-anonymity on the number of mutual friends, which ensures that there exist at least k-1 other friend pairs in the graph that share the same number of mutual friends. We devise algorithms to achieve the k-NMF anonymity while preserving the original vertex set in the sense that we allow the occasional addition but no deletion of vertices. Further we give an algorithm to ensure the k-degree anonymity in addition to the k-NMF anonymity. The experimental results on realword datasets demonstrate that our approach can preserve the privacy and utility of social networks effectively against mutual friend attacks.
Sun et al., REF proposed a promoted anonymization concept, termed as k-NMF anonymity which was designed to solve the problem of mutual friends attacks.
Privacy Preserving Social Network Publication Against Mutual Friend Attacks
2013 IEEE 13th International Conference on Data Mining Workshops
2013 IEEE 13th International Conference on Data Mining Workshops
Computer Science