Under the current background, it is very important to study the key technologies of new power system edge-to-side security protection for massive heterogeneous power IoT terminals and edge IoT agents, including defense technologies at the levels of device ontology security, communication interaction security, and secure access. Meaning. The new power system edge-to-side security protection technology has a summary impact on the privacy protection of indoor positioning. This paper proposes an indoor positioning privacy protection method based on federated learning in Mobile Edge. Computing (MEC) environment. Firstly, we analyze the learning mechanisms of horizontal, vertical, and transfer-federated learning, respectively, and mathematically describe it based on the applicability of horizontal and vertical-federated learning under different sample data characteristics. Then, the risk of data leakage when data are used for research or analysis is greatly reduced by introducing differential privacy. In addition, considering the positioning performance, privacy protection, and resource overhead, we further propose an indoor positioning privacy protection model based on federated learning and corresponding algorithms in MEC environment. Finally, through simulation experiments, the proposed algorithm and other three algorithms are, respectively, compared and analyzed in the case of two identical datasets. The experimental results show that the convergence speed, localization time consumption, and localization accuracy of the proposed algorithm are all optimal. Moreover, its final positioning accuracy is about 94%, the average positioning time is 250 ms, and the performance is better than the other three comparison algorithms.

With the continuous development of mobile communication technology, edge computing technology has been gradually applied to some special business scenarios, such as disaster area rescue and forest fire warning. These application scenarios have certain requirements on the delay of task processing, and need enough network connection and computing resources. Due to the lack of ground network infrastructure, the traditional edge computing scheme can no longer fully meet the needs of users. However, the virtualized environment in cloud computing not only brings convenient services to users, but also faces security threats from different levels. The increasingly rich attack means and the widening of attack surface caused by virtualization have brought new challenges to the security protection of edge computing environment. How to build a secure and credible virtualization environment for cloud computing and alleviate users' concerns about cloud computing security has become an urgent problem to be solved in the further development of cloud computing technology. Based on the analysis of security risks and users' security requirements, this paper puts forward the combination of trusted computing and trust model in cloud computing, to build a safe and credible virtualization environment for users. Experimental results show that the proposed algorithm not only guarantees the validity of the trust model, but also reduces the resource overhead of edge devices in the process of trust evaluation.