A Privacy Measurement Method Using Network Structure Entropy
Data collected by social network services has raised privacy concerns for the individuals nowadays. To preserve privacy of the graph-formatted data, many methods have been proposed by graph modification techniques. Nevertheless, there no general evaluate criterions to measure privacy preserving capability between those methods up to now. In this paper, we introduce a privacy measurement method using network structure entropy which is one of the network static characters in complex network and define a privacy measurement indexes(PMI) to measure the privacy preserving capability of a graph structure. Then, we apply the PMI to a graph privacy preserving grading scheme. Finally, experiments on synthetic graphs data demonstrate that the better privacy capability, the larger network structure entropy. And each kind of graphs correspond to a privacy preserving capability level.