A Security Reputation Model for IoT Health Data Using S-AlexNet and Dynamic Game Theory in Cloud Computing Environment

With the rapid development of cloud computing, users are exposed to increasingly serious security threats such as data leakage and privacy exposure when using cloud platform services. Problems in data security, such as inaccurate screening of indicators, lack of scientific validation of reputation evaluation results are also existed. In order to solve the problems, based on cloud environment, a security reputation model using S-AlexNet convolutional neural network and dynamic game theory (SCNN-DGT) is proposed. And it is used to protect the privacy of health data in Internet of Things (IoT). Firstly, the text information of user health data is pre-classified by using S-AlexNet convolutional neural network. Then, a recommendation incentive strategy based on dynamic game theory is proposed. So that the reputation model of user health data security is built, and the evaluation system of the model is established. Finally, an experimental study is carried out to verify the validity of the model and the model index screening. It is shown by experimental results that the model can solve the problems of low reliability of health data screening index, and low accuracy of credit distinction in cloud environment. Therefore, the reliability of mobile terminals is improved, and data security and privacy protection of mobile cloud services are strengthened effectively.