With the rapid advancement of Internet of Things (IoT) applications, efficient and secure communication is considered a challenging task. Symmetric authenticated key exchange (AKE) is a promising solution due to its lightweight design. However, existing studies have demonstrated that traditional symmetric AKE schemes are unable to achieve perfect forward secrecy (PFS). Although some improved schemes were proposed based on the evolution of long-term secrets, the analysis indicates that there exists a zero-sum trade-off between PFS and self-synchronization. In addition, privacy preservation remains a critical issue. In response, this paper proposes a novel three-party symmetric AKE scheme. Specifically, the secret evolving mechanism to prevent the reverse inference of crucial secret values is constructed in the scheme. Meanwhile, any reachable session state can be self-transferred to the synchronization state at the end of a complete session. The proposed scheme provides anonymity and pseudonym unlinkability to the required party, while also improving the robustness of conditional identity traceability, avoiding false accusations caused by misdirected requests. The formal analysis, heuristic analysis based on the state transition and secrecy dependency, and performance comparison indicate that the proposed scheme achieves essential properties while maintaining manageable overhead.

When decoding neuroelectrophysiological signals represented by Magnetoencephalography (MEG), deep learning models generally achieve high predictive performance but lack the ability to interpret their predicted results. This limitation prevents them from meeting the essential requirements of reliability and ethical-legal considerations in practical applications. In contrast, intrinsically interpretable models, such as decision trees, possess self-evident interpretability while typically sacrificing accuracy. To effectively combine the respective advantages of both deep learning and intrinsically interpretable models, an MEG transfer approach through feature attribution-based knowledge distillation is pioneered, which transforms deep models (teacher) into highly accurate intrinsically interpretable models (student). The resulting models provide not only intrinsic interpretability but also high predictive performance, besides serving as an excellent approximate proxy to understand the inner workings of deep models. In the proposed approach, post-hoc feature knowledge derived from post-hoc interpretable algorithms, specifically feature attribution maps, is introduced into knowledge distillation for the first time. By guiding intrinsically interpretable models to assimilate this knowledge, the transfer of MEG decoding information from deep models to intrinsically interpretable models is implemented. Experimental results demonstrate that the proposed approach outperforms the benchmark knowledge distillation algorithms. This approach successfully improves the prediction accuracy of Soft Decision Tree by a maximum of 8.28%, reaching almost equivalent or even superior performance to deep teacher models. Furthermore, the model-agnostic nature of this approach offers broad application potential.

Information-theoretic secure (ITS) authentication is an essential part of a quantum key distribution (QKD) system. In the QKD network, the authentication scheme based on the ITS QKD keys pre-shared by the QKD system. However, such a scheme is subject to the limitation that consumes a significant amount of authentication keys when the scale and communication requirements of QKD networks grow. To address the issue, we propose an ITS group authentication scheme for QKD networks that consumes considerably fewer keys. In our scheme, multiple QKD network nodes collaborate and complete the authentication as a group. Compared to the pre-shared keys authentication scheme, our scheme reduces the complexity of authentication key consumption from$O\left ({N^{2}d }\right) $to$O\left ({N }\right) $, where$N $is the number of nodes and$d $is the average path length of the network topology. The security of the proposed scheme is analyzed in detail and simulation experiments are conducted to demonstrate our scheme’s effectiveness for varying networks scales and communication requirements.

Deep learning has greatly enhanced the predictive performance of magnetoencephalography (MEG) decoding. However, the lack of interpretability has become a major obstacle to the practical application of deep learning-based MEG decoding algorithms, which may lead to non-compliance with legal requirements and distrust among end-users. To address this issue, this article proposes a feature attribution approach, which can provide interpretative support for each individual MEG prediction for the first time. The approach first transforms a MEG sample into a feature set, then assigns contribution weights to each feature using modified Shapley values, which are optimized by filtering reference samples and generating antithetic sample pairs. Experimental results show that the Area Under the Deletion test Curve (AUDC) of the approach is as low as 0.005, which means a better attribution accuracy compared to typical computer vision algorithms. Visualization analysis reveals that the key features of the model decisions are consistent with neurophysiological theories. Based on these key features, the input signal can be compressed to one-sixteenth of its original size with only a 0.19% loss in classification performance. Another benefit of our approach is that it is model-agnostic, enabling its utilization for various decoding models and brain-computer interface (BCI) applications.