Centrality Based Privacy Preserving for Weighted Social Networks

Abstract

Virtual assets accounted for a larger proportion in the production and life. To protect privacy of virtual assets this paper proposed privacy preserving methods using the centrality based on complex network theory. On the basis of not changing the network structure, some properties called centrality of the network topology is treated as noise to made the corresponding weight perturbation to protect the shortest path in the network. And changing of the length of the shortest path lies in a certain range is analyzed. Result of the experiment shows that the perturbation strategy performs great for the protection of the shortest path in social networks, better than the Gauss perturbation strategy.