DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

In recent years, edge computing transforms the network edge into an intelligent platform to provide better services for users by making storage, computing, control and network function closer to end-users, things, and sensors. However, due to the limitation of edge resources and the diversity of edge resource providers, there is no good mechanism to solve the resource allocation problem in the edge computing resource allocation market. Aiming at the problem of resource allocation in edge computing, this paper proposes a double auction scheme-DPODA. This scheme establishes the auction market of edge computing resources, which can improve the social welfare of auction to the maximum extent. Moreover, as the private information of users in the auction market may affect market transactions, in order to solve this problem, this paper uses differential privacy technology to effectively protect the privacy of both sides of the auction, so as to avoid the unfairness in the auction market. A large number of simulation experiments show that the algorithm proposed in this paper not only achieves the research purpose but also ensures the privacy information security of the auctioneer.