Differential privacy protection on weighted graph in wireless networks

With the development of 5G communication technology, the Internet of Things technology has ushered in the development opportunity. In the application of Internet of Things, spatial and social relations can be used to provide users with convenience in life and work, meanwhile there is also the risk of personal privacy disclosure. The data transmitted in the wireless network contains a large number of graph structure data, and the edge weight in weighted graph increases the risk of privacy disclosure, therefore in this paper we design a privacy protection algorithm for weighted graph, and adopts the privacy protection model to realize the privacy protection of edge weight and graph structure. Firstly, the whole graph sets are disturbed and the noises are added during the process of graph generation. Secondly, the privacy budget is allocated to protect the weight values of edges. The graph is encoded to deal with the structure of graph conveniently without separating from the information of edges, and then the disturbed edge weight is integrated into the graph. After that the privacy protection of the graph structure is realized in the process of frequent graph mining combined with differential privacy. Finally, the algorithm proposed in this paper is validated by experiments.