ABSTRACT The rapid development of big data technologies has exacerbated the challenge of maintaining patient privacy in smart healthcare environments. Although previous mutual patient–physician authentication systems achieve basic anonymization, patients' communication addresses are still exposed, and attackers can analyze transaction records to establish correlations between users' addresses and even obtain their real identities. To address this problem, we propose a user anonymization scheme based on the elliptic curve discrete logarithmic problem assumption, which aims to prevent malicious interception and theft of patients' personal data by obfuscating the identity of registered users. By combining identity‐based encryption with advanced anonymization techniques and reconstructing signatures of knowledge, traceability is achieved while ensuring that only the intended recipient with the corresponding private key can decrypt the data. The validation shows that our system guarantees unlinkability and anonymity while resisting hijacking attacks and man‐in‐the‐middle attacks, and it is simulated using JPBC 2.0.0 (Jdk version 14.0.1), which shows that the communication overhead needs 808 bytes and that the computation overhead for system initialization, signature, and validation are 102, 167, and 70 ms, respectively.

To address the challenge of cyberattacks, intrusion detection systems (IDSs) are introduced to recognize intrusions and protect computer networks. Among all these IDSs, conventional machine learning methods rely on shallow learning and have unsatisfactory performance. Unlike machine learning methods, deep learning methods are the mainstream methods because of their capability to handle mass data without prior knowledge of specific domain expertise. Concerning deep learning, long short-term memory (LSTM) and temporal convolutional networks (TCNs) can be used to extract temporal features from different angles, while convolutional neural networks (CNNs) are valuable for learning spatial properties. Based on the above, this paper proposes a novel interlaced and spatiotemporal deep learning model called CRGT-SA, which combines CNN with gated TCN and recurrent neural network (RNN) modules to learn spatiotemporal properties, and imports the self-attention mechanism to select significant features. More specifically, our proposed model splits the feature extraction into multiple steps with a gradually increasing granularity, and executes each step with a combined CNN, LSTM, and gated TCN module. Our proposed CRGT-SA model is validated using the UNSW-NB15 dataset and is compared with other compelling techniques, including traditional machine learning and deep learning models as well as state-of-the-art deep learning models. According to the simulation results, our proposed model exhibits the highest accuracy and F1-score among all the compared methods. More specifically, our proposed model achieves 91.5% and 90.5% accuracy for binary and multi-class classifications respectively, and demonstrates its ability to protect the Internet from complicated cyberattacks. Moreover, we conduct another series of simulations on the NSL-KDD dataset; the simulation results of comparison with other models further prove the generalization ability of our proposed model.

As Internet of Things (IoT) technology continues to advance swiftly, a growing array of IoT devices is being assimilated into the industrial IoT framework. However, ensuring secure and efficient transmission of data from IoT devices has become a key technological focus in current research on the development of industrial IoT. Attribute encryption technology is an effective encryption technique that ensures data security; meanwhile, proxy re-encryption technology allows proxies to encrypt original ciphertext into new ciphertext without accessing any plaintext information. The combination of these two technologies enables secure transmission of data among users with different attributes. However, the high computational overhead remains a significant factor limiting the development of this solution. IoT data needs to be shared not only among different units within the same domain but also across units in different domains. Securing shared data has emerged as a pivotal area of focus in contemporary research. In this article, we propose an improved fine-grained proxy re-encryption scheme for cross-attribute domains, which not only facilitates simple cross-domain data sharing but also enhances encryption efficiency. Security proofs demonstrate the adequacy of our proposed scheme, and performance analysis indicates its superiority over other solutions in certain aspects.

In this paper, we study a novel transmission framework based on statistical channel state information (SCSI) by incorporating edge caching and beamforming in a fog radio access network (F-RAN) architecture. By optimizing the statistical beamforming and edge caching, we formulate a comprehensive nonconvex optimization problem to minimize the backhaul cost subject to the BS transmission power, limited caching capacity, and quality-of-service (QoS) constraints. By approximating the problem using the l 0 -norm, Taylor series expansion, and other processing techniques, we provide a tailored second-order cone programming (SOCP) algorithm for the unicast transmission scenario and a successive linear approximation (SLA) algorithm for the joint unicast and multicast transmission scenario. This is the first attempt at the joint design of statistical beamforming and edge caching based on SCSI under the F-RAN architecture.

In this paper, we study the degrees of freedom (DoF) of a new network information flow model named the circular multirelay multiple-input multiple-output interference channel (CMMI). In this model, there are two clusters and each of them contains three users. Each user equipped with M antennas in one cluster intends to deliver data streams to another user in the same cluster in a circular one-way transmission via the common distributed K N-antenna relay nodes. The CMMI network model can be considered as a basic component to construct the complicated Internet of Things networks. By assuming linear processing at the users and the relays, we show that the original analysis of DoF comes down in finding solutions of some nonlinear matrix equations with rank constraints. Toward this end, by using linear precoding and post-processing techniques, we propose two different approaches to solve the nonlinear matrix equations based on different antenna configurations. We show that a √ DoF of max{min{M, (√6K/12)}, min{(M/3), (KN/2)}} is achievable for ∀(M/N) ∈ (0, +∞). In addition, to assess the optimal DoF, the cut-set approach is used for deriving the DoF upper bound by innovatively separating certain users to form two-pair two-way relay channels. We show that the DoF of CMMI is upper bounded by max{min{M, (KN/3)}, min{(2M/3), (KN/2)}}. By combining the achievable DoF and the upper bound, we finally show that the optimal DoF of CMMI can be achieved √ for (M/N)∈[0, (√6K/12)]∪[(3K/2), +∞), ∀K ≥ 1.