Dynamic Network Data Protection Algorithm Using Differential Privacy in Internet of Things

Abstract

With the development of Internet of things, more and more personal information needs to be collected in the process of applying Internet of things technology, which brings about the problem of data privacy protection. The algorithm proposed in this paper uses the differential privacy model to process the information network collected by the wireless sensor network terminal in the perception layer of the Internet of things and transmits it to the network layer to achieve the purpose of information protection. In order to reduce the added differential privacy noise, this paper firstly makes dynamic community discovery of the network graph, and adds more noise to the edge of nodes within the community. Then the hierarchical random graph model is used to represent the network graph, and the noise is added to the connection probability between the nodes in the graph, instead of adding noise to the edge directly. Considering the dynamic change of the information collected by the wireless sensor network, this paper proposes a time window partition and dynamic network community discovery algorithm, and publishes a purification map for each time window. Through hierarchical sampling, the time cost is reduced and the cumulative error is reduced, while the network structure characteristics of the time window are maintained as far as possible. Theoretical analysis and experiments show that the algorithm proposed in this paper can preserve the important network structure characteristics of the original graph under the premise of satisfying the differential privacy protection model.