Link privacy in social networks
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In this paper, we revisit the problem of the link privacy attack in online social networks. In the link privacy attack, it turns out that by bribing or compromising a small number of nodes (users) in the social network graph, it is possible to obtain complete link information for a much larger fraction of other non-bribed nodes in the graph. This can constitute a significant privacy breach in online social networks where the link information of nodes is kept private or accessible only to closely related nodes. We show that the link privacy attack can be made even more effective with degree inference. Since online social networks typically have high degree, the link privacy attack becomes quite feasible even with an in-lookahead neighborhood of one (only friends can see a user's links/profile). To reduce the effect of the link privacy attack, we present several practical mitigation strategies -- non-uniform user privacy settings, approximation of the node degree information and a non-constant cost model for the attack. All the strategies are able to mitigate the privacy link attack by either reducing the effectiveness of the attack or by making it more expensive to mount. Interestingly, some of the more efficient strategies now become worse than the RANDOM strategy and the effect of a larger neighborhood which would otherwise make the attack even more efficient can be mitigated.