Trusted Computing and Privacy Protection of Computer Internet of Things Nodes Based on Deep Fuzzy Control of Dynamic Learning Rate

Abstract

Under the background of mobile Internet of Things, this paper focuses on the solutions of trusted computing and privacy protection of Internet of Things nodes. Based on the research of existing mainstream service discovery protocols and the deep fuzzy control theory of dynamic learning rate, this paper proposes a trusted computing algorithm for Internet of Things nodes in mobile Internet of Things,which takes into account service quality and user preference.This paper combines the depth fuzzy control model of IoT nodes proposed before, uses a normal data set to train it, and then makes it generate and play the prediction residuals. It will be further used to build a privacy protection model and realize the anomaly detection of privacy protection data in the Internet of Things. This paper designs and implements a trusted computing system using the Internet of Things platform,which has been tested. Experimental results show that, compared with previous elastic matching algorithms, the precision and recall of the new algorithm proposed in this paper are improved.