Target Privacy Preserving for Social Networks

In this paper, we incorporate the realistic scenario of key protection into link privacy preserving and propose the target-link privacy preserving (TPP) model: target links referred to as targets are the most important and sensitive objectives that would be intentionally attacked by adversaries, in order that need privacy protections, while other links of less privacy concerns are properly released to maintain the graph utility. The goal of TPP is to limit the target disclosure by deleting a budget limited set of alternative non-target links referred to as protectors to defend the adversarial link predictions for all targets. Traditional link privacy preserving treated all links as targets and concentrated on structural level protections in which serious link disclosure and high graph utility loss is still the bottleneck of graph releasing today, while TPP focuses on the target level protections in which key protection is implemented on a tiny fraction of critical targets to achieve better privacy protection and lower graph utility loss. Currently there is a lack of clear TPP problem definition, provable optimal or near optimal protector selection algorithms and scalable implementations on large-scale social graphs.Firstly, we introduce the TPP model and propose a dissimilarity function used for measuring the defense ability against privacy analyzing for the targets. We consider two different problems by budget assignment settings: 1) we protect all targets and to optimize the dissimilarity of all targets with a single budget; 2) besides the protections of all targets, we also care about the protection of each target by assigning a local budget to every target. Moreover, we propose two local protector selections, namely cross-target and with-target pickings. Each problem with each protector picking selection is corresponding to a greedy algorithm. We also implement scalable implementations for all greedy algorithms by limiting the selection scale of protectors, and we prove that all greedy-based algorithms achieve approximation by holding the monotonicity and submodularity. Through experiments on large real social graphs, we demonstrate the effectiveness and efficiency of the proposed target link protection methods.