Increasing the Accuracy of Secure Model for Medical Data Sharing in the Internet of Things

Abstract

The security of medical data sharing (MDS) plays an important role in the area of healthcare. Significantly, achieving its security faces more challenges due to the feature of multiparty holding, higher complexity, and serious data silos. Different from traditional secure schemes, which established model cannot deal with the above three problems due to the low accuracy of the MDS secure model, this paper designs a novel secure MDS model and two schemes to increase the accuracy of the model. In detail, to eliminate the issues of data silos and point failure, we combine the federated learning (FL) with blockchain technology into MDS secure model, and the data confidentiality of the exchanged data in the process of FL can be further ensured by differential privacy (DP). Then, to increase the accuracy of the secure MDS model, we design a validation incentive mechanism based on model quality (VIM) and an effective DP method with assigned weights (AWDP), in terms of participants’ enthusiasm and noise accumulation, respectively. Simulations show that the established model is effective and correct and the designed VIM and AWDP can achieve higher accuracy than current popular methods, resulting in 30% increment.