Collective Influence Based Privacy Preservation for Social Networks

Privacy protection of sensitive information in social networks has become an urgent problem to be solved. The existing social network privacy protection schemes can guarantee data security to a certain extent, but will cause noise redundancy, which means data security and availability cannot be well balanced. In order to effectively reduce noise redundancy and take data security and availability into account, a privacy protection algorithm based on collective influence(CI) is proposed. CI is used as the noise sources on edge weight perturbation after randomization and normalization. Then the node perturbation strategy is added based on edge weight perturbation strategy: CI is used as the discriminant index to identify redundant vertices in the network, then some redundant vertices are removed. The perturbation strategy proposed can retain better data availability and greatly improve data security when achieving the privacy protection for both vertices and edge weights compared with the existing strategy.