Signcryption technology combines signature and encryption operations in a single step to achieve message authentication and confidentiality. The ordinary signcryption technology cannot realize communication between two different cryptographic systems. Therefore, to implement efficient communication between different cryptosystems and resist quantum attacks, this paper proposes a lattice-based efficient heterogeneous signcryption scheme. The heterogeneous signcryption scheme is proved to be secure assuming the hardness of small integer solution and learning with errors problems. Then this paper applies the lattice-based efficient heterogeneous signcryption scheme to the federated learning system to achieve the transmission of confidential information, and designs an intelligent federated learning system on lattice-based efficient heterogeneous signcryption. This system realizes federated learning and the quantum security of data transmission while preserving private data.

It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an all-round way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.

The application of digital tracking mechanism introduces a series of leakage problems of users' personal sensitive information related to the trajectory. Therefore, we propose a secure storage scheme for trajectory data. Firstly, four-dimensional spatiotemporal clustering of the trajectory data is performed to reduce the spatiotemporal complexity of data storage. Secondly, the privacy level of the trajectory data in the clusters is measured individually, which ensures the needs for personalized privacy protection are met. Finally, a noise trajectory (NTR) tree based on differential privacy is constructed, and the allocation of privacy budget and noise addition are optimized. Extensive simulations show that our scheme improves in terms of time efficiency, and achieves a flexible and effective balance between data accuracy and privacy.

Due to the openness nature of wireless medium, security issues have increasingly become a bottleneck that restricts the development of ultra-reliable and low-latency communications (uRLLCs). Physical layer security (PLS) technique has been proposed to fulfill the security and confidentiality of information transmission by exploiting the characteristics of the wireless channel, which caters to the features of uRLLC. Furthermore, PLS also shows great practicality in artificial intelligence field, especially in wireless intelligent networks. However, the previous works on the study of PLS ignored the significance of mobility and limited packet length constraint required by uRLLC for satisfying low latency, in this paper, we investigate the impact of mobility on the secrecy performance of uRLLC by using the Random WayPoint (RWP) model and the Random Direction (RD) model. Specifically, with the tools of stochastic geometry, to observe the impact of key system parameters on the secrecy performance, we establish the closed-form expression of connection outage probability, secrecy outage probability, and decoding error probability-based secrecy transmission capacity (DEP-STC). Furthermore, we derive the condition that achieves a positive DEP-STC under two moving models, which can offer the network designer some greatly significant insights into achieving perfect secrecy. Simulations validate our derived theoretical results, and indicate that RWP moving receiver can obtain a higher security level than RD moving one, while RWP eavesdropper can lead to a lower security.