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.

随着5G通信技术的发展，物联网技术迎来了发展机遇。在物联网的应用中，空间和社会关系可以用来为用户提供生活和工作上的便利，同时也存在个人隐私泄露的风险。无线网络中传输的数据包含大量的图结构数据，加权图中的边缘权重增加了隐私泄露的风险，因此本文设计了加权图的隐私保护算法，并采用了隐私保护模型实现边缘权重和图形结构的隐私保护。首先，在图的生成过程中，整个图集受到干扰，并且添加了噪声。其次，分配隐私预算以保护边缘的权重值。对图进行编码以方便地处理图的结构，而又不脱离边缘信息，然后将受干扰的边缘权重集成到图中。之后，在频繁图挖掘与差分隐私相结合的过程中，实现了图结构的隐私保护。最后，通过实验对本文提出的算法进行了验证。