Privacy protection in social networks using l-diversity

Abstract

With the increasing popularity of online social networks, such as twitter and weibo, privacy preserving publishing of social network data has raised serious concerns. In this paper, we focus on the problem of preserving the sensitive attribute of the node in social network data. We call a graph l-diversity anonymous if all the same degree nodes in the graph form a group in which the frequency of the most frequent sensitive value is at most . To achieve this objective, we devise an efficient heuristic algorithm via graphic l-diverse partition and also use three anonymous strategies(AdjustGroup, RedirectEdges, AssignResidue)to optimize the heuristic algorithm. Finally, we verify the effectiveness of the algorithm through experiments. © 2012 Springer-Verlag.