Link disclosure between two individuals in a social network could be a privacy breach. To limit link disclosure, previous works modeled a social network as an undirected graph and randomized a link over the entire domain of links, which leads to considerable structural distortion to the graph. In this work, we address this issue in two steps. First, we model a social network as a directed graph and randomize the destination of a link while keeping the source of a link intact. The randomization ensures that, if the prior belief about the destination of a link is bounded by some threshold, the posterior belief, given the published graph, is no more than another threshold. Then, we further reduce structural distortion by a subgraph-wise perturbation in which the given graph is partitioned into several subgraphs and randomization of destination nodes is performed within each subgraph. The benefit of subgraph-wise perturbation is that it retains a destination node with a higher retention probability and replaces a destination node with a node from a local neighborhood. We study the trade-off of utility and privacy of subgraph-wise perturbation.
To protect from link disclosure Author proposes Subgraph wise perturbation and perturbation process REF , in which the given graph is partition into local sub-graphs and graph wise perturbation is apply to each sub graph.
Limiting link disclosure in social network analysis through subgraph-wise perturbation
EDBT '12
Computer Science