The explosive growth of multimodal web data demands communication that transmits meaning rather than raw bits. Existing semantic-communication systems often fail under noise, missing modalities, and distribution shifts because they optimize surface features instead of modality-invariant knowledge. We present Grasp, a knowledge-centric framework for cross-modal communication. Grasp segments streams into semantic blocks and builds a graph over them; a lightweight Graph Neural Networks (GNN) produces schedulable, importance-weighted representations. At its core is knowledge purification : we minimize a conditional mutual information upper bound to perform a three-way disentanglement—strongly related, weakly related, and task-irrelevant components—so that only essential semantics are transmitted while non-essential factors are suppressed. To maintain synchrony, we introduce one-to-two temporal contrastive learning to achieve triple alignment of video, audio, and text despite sampling asynchrony. For efficient transmission, Grasp uses a cross-modal shared vector-quantization codebook—a discrete knowledge codebook —updated by multimodal attention. At the receiver, a soft-recovery mechanism leverages this shared knowledge to robustly reconstruct semantics under low signal-to-noise ratio (SNR) or missing modalities, yielding graceful degradation. Across web tasks—including cross-modal retrieval and missing-modality inference—Grasp improves knowledge consistency, semantic fidelity, and downstream performance over strong baselines while maintaining low latency. These results show that communication structured around purified knowledge is key to building robust, semantic-aware systems for the modern web.

This paper proposes CADiS, a causality-driven anomaly detection framework, to address the challenges of root cause identification in high-dimensional Industrial Internet of Things (IIoT) multivariate time series. The essential difference between CADiS and existing correlation-driven deep models lies in its core innovation: it fundamentally redefines anomalies as the structural decay of an underlying causal mechanism, rather than merely capturing symptomatic deviations or spurious correlations. Specifically, the framework first learns a directed and lag-aware causal prior from normal data, compiling it into a structured attention mask to constrain information flow. Then, a Causal-Phase Decomposition (CPD) technique treats each time window as a micro-experiment, comparing an ante-phase with a post-phase to explicitly capture the dynamics of causal attenuation. Inference relies on a unified Causal-Change Score (CCS), which quantifies the degradation of causal association strength, directly revealing the breakdown of the system’s causal logic. Furthermore, the decomposed causal change matrix allows for fine-grained and auditable root cause diagnosis. Extensive experiments on real-world industrial datasets demonstrate that CADiS significantly outperforms strong baselines, achieving theVROCof 89.93% andVPRof 76.93% on SWaT; TheAPRof 18.04% andVROCof 78.38% on SMD, thereby validating its robustness and diagnostic precision.

Personalized Federated Learning (pFL) tailors models to each client’s local data distribution in heterogeneous federated learning settings. Federated Meta-Learning (FML) is a branch of pFL that uses meta-learning to achieve fast adaptation, where clients start with a meta-model and personalize it by fine-tuning it with local data. Since a single global meta-model has limitations when the data distribution of clients varies significantly, meta-model personalization should be considered in FML. However, most benchmark pFL methods lack meta-model personalization, and usually lack meta-learning or relying on a single global meta-model. Besides, these methods can neither provide meta-model personalization nor guarantee generalization and convergence, due to the challenges in measuring the distance between the meta-model and the client model in FML. To address these issues, we combine FML with hypernetwork and propose a constrained hypernetwork-based FML framework called FMLH, which innovatively utilizes hypernetwork to capture the differences in fine-tuned models, thereby providing personalized meta-models for each client. We provide rigorous mathematical proofs illustrating how the hypernetwork affects the convergence and generalization bounds of FMLH. Experimental results demonstrate that FMLH significantly improves the generalization of the model in cross-client shifts, with the lowest decile accuracy improved by up to 18.71%. FMLH also outperforms representative pFL algorithms by up to 5.6% in terms of maximum accuracy improvement.

Despite progress in global healthcare systems, the utilization of domestic healthcare facilities remains limited in several regions, with a considerable proportion of people pursuing treatment overseas. This development highlights the necessity for systematic incorporation of patient-centered input, an essential element for enhancing accountability, transparency, and quality in local healthcare. Our research seeks to address this deficiency by establishing a system that collects and analyzes patient feedback to inform and improve healthcare policies and practices, particularly in areas with elevated demand for medical services. We offer an effective platform for viewing reviews from different hospitals, especially in places where people routinely visit for medical services. Therefore, we built our primary "Dhaka Private Hospitals Review Dataset," considering gathering and evaluating patient opinions methodically. We further employ transformer-based generative adversarial learning to evaluate sentiment analysis using knowledge distillation (KD) to boost model efficiency. Our proposed GANBERT architecture includes two optimized student models gated recurrent unit-based Contextualized BERT (GC-BERT) and LSTM-based Contextualized BERT (LC-BERT) with enhanced generators and discriminators. Our GC-BERT enhances execution time by 1.27% to 24.27%, while LC-BERT improves by 14.13% to 23.30%, showing superior advancements compared to other contemporary models. Each model with reductions ranging from 82.50% to 99.99% parameters, making them lightweight and efficient compared to other teacher models in the KD process. Instead of using contextual word representations which demand more space and complexity for reviewing patient feedback, we utilize the single static pretrained and low-dimensional word embedding space approach integrating student models.

As the core infrastructures in the information systems, the data center networks carry a large number of tasks of data processing and storage. In a data center network, intermittent faults are often difficult to be found and dealt with in time because of their hiddenness and uncertainty. Once these faults accumulate to a certain extent, they can cause serious network outages and even lead to the collapse of the entire data center. In order to discover and resolve these potential problems in a timely manner, it is crucial to apply intermittent fault diagnosis, thus ensures the continuous and stable operation of the data center. In this paper, we propose the intermittent fault diagnosabilitytPMCI(Cn) for ann-dimensional data center network CSDC under the Preparata/Metze/Chien model (PMC model) by establishing the fault tolerance of the network. Additionally, under the PMC model, we propose a probabilistic multiple intermittent fault diagnosis algorithm (PMIFDPMC) with time complexityO(nN) by preferentially generating weighted multiple test networks (GWMTN) whereNis the scale of CSDC. Moreover, we apply the algorithm PMIFDPMC to a 7-dimensional CSDC and a real-world dataset of the Internet of Things. Across different scenarios of intermittent fault nodes, we calculate the Accuracy, Recall, FNR, G-mean, and F1-score using various testing iterations. The experimental results demonstrate that, as the number of testing iterations of algorithm PMIFDPMC increases, the quantity of intermittent fault nodes that are correctly diagnosed also increases. This highlights the favorable performance and effectiveness of algorithm PMIFDPMC on the real-world dataset of the Internet of Things.

Federated learning is vulnerable to model poisoning attacks in which malicious participants compromise the global model by altering the model updates. Current defense strategies are divided into three types: aggregation-based methods, validation dataset-based methods, and update distance-based methods. However, these techniques often neglect the challenges posed by device heterogeneity and asynchronous communication. Even upon identifying malicious clients, the global model may already be significantly damaged, requiring effective recovery strategies to reduce the attacker's impact. Current recovery methods, which are based on historical update records, are limited in environments with device heterogeneity and asynchronous communication. To address these problems, we introduce FedHAN, a reliable federated learning algorithm designed for asynchronous communication and device heterogeneity. FedHAN customizes sparse models, uses historical client updates to impute missing parameters in sparse updates, dynamically assigns adaptive weights, and combines update deviation detection with update prediction-based model recovery. Theoretical analysis indicates that FedHAN achieves favorable convergence despite unbounded staleness and effectively discriminates between benign and malicious clients. Experiments reveal that FedHAN, compared to leading methods, increases the accuracy of the model by 7.86%, improves the detection accuracy of poisoning attacks by 12%, and enhances the recovery accuracy by 7.26%. As evidenced by these results, FedHAN exhibits enhanced reliability and robustness in intricate and dynamic federated learning scenarios.

Federated learning, as a distributed learning framework, aims to develop a global model while preserving client privacy. However, heterogeneity of client data leads to fairness issues and reduced performance. Techniques like parameter decoupling and prototype learning appear promising, yet challenges such as forgetting historical data and limited generalization persist. These methods also lack local insights, with locally trained features prone to overfitting, which affects generalization in global parameter aggregation. To address these challenges, we propose FedCPD, a personalized federated learning framework. FedCPD maintains historical information, reduces information loss, and increases personalization through hierarchical feature distillation and cross-layer feature fusion. Moreover, we utilize representation techniques like prototype contrastive learning and prototype alignment to capture diverse client data features, thus improving model generalization and fairness. Experiments show FedCPD outperforms state-of-the-art models, enhancing generalization by up to 10.40% and personalization by up to 4.90%, highlighting its effectiveness and superiority.

Model inversion and adversarial attacks in semantic communication pose risks, such as content leaks, alterations, and prediction inaccuracies, which threaten security and reliability. This paper introduces, from an attacker's viewpoint, a novel framework called RepObE (Representation Learning-Enhanced Obfuscation Encryption Modular Semantic Task Framework) to secure semantic communication. This framework employs dynamic encryption during semantic extraction and feature transmission to hinder attackers from reconstructing data through eavesdropping, thus strengthening system privacy. To combat image communication task challenges, we propose a prototype adversarial collaborative alignment training approach enhanced by representation learning. This method extracts and encodes semantic features while using dynamic perturbation and robust optimization to improve system resilience against adversarial threats. The approach ensures reliable semantic communication in complex environments, maintaining performance while countering attacks using feature obfuscation, adversarial training, and representation learning. Experimental results demonstrate that our method surpasses existing techniques by more than 2% in resisting model inversion attacks on classification tasks. Visually, our method excels with minimal decipherable images for attackers. It also shows a 3% to 5% improvement in countering adversarial attacks on classification tasks.

Rapid iterations and updates in both software and hardware, along with significant advancements in communication technology, have given rise to the concepts of symbiotic Internet of Things (IoT) and ubiquitous interconnectivity, providing strong evidence for the flourishing development of the Internet of Things. However, the limited resources and computing capabilities, along with the heterogeneity of deployment environments, make symbiotic IoT devices more susceptible to security threats and operational issues. Intermittent failures are especially prevalent in the symbiotic IoT, leading to more significant risks for devices. In this paper, we present an IFDGAT-LSTM (Intermittent Fault Diagnosis Based on Long Short-Term Memory and Graph Attention Network) framework for diagnosing intermittent failures in wireless sensing devices within the symbiotic IoT. The framework is based on a graph neural network and takes into account not only the time series characteristics of symbiotic IoT devices but also their deployment topology. By incorporating both aspects, we achieve more accurate diagnostics of intermittent failures in the symbiotic IoT, thus enhancing its reliability. Firstly, we propose the concept of a quasi-dynamic graph based on the variations in the topology within the symbiotic IoT. Subsequently, we introduce an intermittent failure diagnosis framework that combines a graph neural network to identify intermittent failure nodes within the quasi-dynamic graph. Finally, we performed experiments on the WADI symbiotic IoT dataset to evaluate the performance of our model in diagnosing intermittent failure nodes. We used the precision, recall, and F1 score metrics for assessment. The experimental outcomes show that our proposed model, IFDGAT-LSTM, achieves an Precision of 99.58% in diagnosing intermittent failure nodes. This highlights the strong performance and efficacy of the IFDGAT-LSTM model.

The rapid proliferation of Internet of Things (IoT) systems has introduced complex networks of interconnected devices, computational resources, and web-based communication infrastructure. Privacy protection in IoT data routing is critical to enabling secure deployment of large language models (LLMs) for processing distributed sensor data, user queries, and device-generated content. However, IoT environments inherently involve heterogeneous devices, dynamic network topologies, and resource-constrained nodes, complicating the design of privacy-preserving routing mechanisms that simultaneously ensure reliability across diverse communication layers. To address these challenges, we propose an innovative FtPR (Fault-tolerant Privacy Routing) model based on secure multiparty computing mechanism, which enables secure and efficient data fusion and transmission in IoT networks. FtPR establishes a novel connection between IoT device clusters and data center network architecture AQDNn routers, leveraging the hierarchical architecture of AQDNn to construct completely independent spanning trees (CIST). By exploiting the non-overlapping paths between nodes in distinct CISTs, FtPR achieves fault-tolerant routing while maintaining privacy guarantees. Building on this framework, we introduce a secure multiparty computing mechanism to perturb link weights in the AQDNn. This ensures that link weights across different CISTs adhere to constrained ranges, preventing adversarial inference of routing paths. Each node operates with localized knowledge of its connected link weights, eliminating the need for global network visibility. Consequently, even if malicious actors compromise one or multiple nodes, they cannot reconstruct end-to-end communication paths, thereby preserving route anonymity. Experimental results demonstrate that FtPR improves IoT network performance and security, reducing misclassification rates and marginal release score compared to state-of-the-art methods.

The introduction of semantic communication offers an effective solution for achieving efficient and reliable information transmission in the social Internet of Things (SIoT). SIoT combines social networks with the Internet of Things (IoT) to create a “social network of smart objects,” utilizing analytical and statistical models to provide efficient and scalable services. However, ensuring high-quality and reliable data transmission within the SIoT remains a significant challenge. Semantic communication methods can effectively address this issue. Semantic communication represents an advanced paradigm aimed at achieving reliable transmission through semantic-level data compression. In this article, we propose a semantic communication framework based on adaptive federated deep learning. This framework combines source-channel joint coding with channel bandwidth adaptation techniques to enhance transmission efficiency and promote natural and effective information exchange. Specifically, deep reinforcement learning is employed to manage dynamic bandwidth allocation, enabling the selection of optimal bandwidth under varying signal-to-noise ratios and data conditions, thereby improving transmission quality and bandwidth utilization. Additionally, we introduce a training method based on federated learning to enhance the model’s generalization ability under different channel conditions. Simulation results demonstrate that our proposed method outperforms traditional models, exhibiting excellent adaptability to low signal-to-noise ratios and low bandwidth environments, as well as higher stability. This positions our method as a valuable approach for ensuring reliable data communication in the SIoT.

In autonomous driving environments, generative adversarial networks (GANs) are often used to predict the future trajectories of objects in the scene, providing decision support for autonomous driving systems. However, integrating GAN models into the Internet of Vehicles (IoV) poses numerous challenges. First, GAN models necessitate user data and extensive computing resources, whereas diverse intelligent connected vehicle (ICV) possess limited bandwidth and computational capabilities, making it challenging to deploy models of the same scale as those in the cloud. Second, multifaceted aspects, including energy consumption, computation, communication, and vehicle training scheduling, have yet to be thoroughly examined, particularly in the context of IoV’s limited resources. To address the above issues, we propose a novel federated learning framework, heterogeneous-vehicle-scheduling-GAN (HVS-GAN), for training GANs in resource-constrained IoV environments. HVS-GAN balances GAN generation quality and training costs in IoV. It supports multiple ICVs training GAN models of different structures, breaking the strong assumption of uniform GAN model size constraints in previous works and enabling collaborative learning within IoV. Furthermore, to balance quality and training costs, we incorporate deep deterministic policy gradients learning to manage varying model size constraints, training delays, and training consumption across participating ICVs. Experimental results and analysis confirm the superiority of our proposed HVS-GAN solution, which achieves better outcomes in IoV scenarios with stringent model size constraints compared to state-of-the-art algorithms.

Federated Learning is an emerging distributed algorithm that is designed to collaboratively train the global model without accessing clients’ private data. However, heterogeneity of data among clients leads to significant degradation in model performance. Some studies suggest adopting model regularization and using generators to enrich datasets with diverse features can effectively enhance model performance. But current research focuses on regularizing specific modules of the model, failing to achieve regularization across the entire model, and offering limited mitigation of bias from heterogeneous data. Moreover, few methods consider that generators often produce samples with simple features, and the direct use for generating raw data can raise privacy concerns. To solve these challenges, we propose a generator-based heterogeneous Federated Prototype Adversarial Learning framework, named FedPA, which combines prototype learning and lightweight generators to achieve regularization of the entire model. Our generators are designed to generate features rather than raw data, and use prototype learning to find the hard features in an adversarial learning manner, thereby improving model performance. Experimental results show that FedPA improves test accuracy by 3.7% compared to state-of-the-art methods, validating that FedPA can effectively mitigate model bias.

Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resource-intensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

Autonomous Transportation Systems (ATS) make the transportation system transition from “passive transportation” to “autonomous service”. The wireless nature of communication in ATS presents significant cybersecurity challenges. Conventional intelligent vehicular intrusion detection methods may not suffice in situations where vehicular data is produced at an unprecedented scale and diverse cybersecurity threats are launched. Therefore, there is a demand for the creation of advanced intelligent vehicular intrusion detection systems that can effectively manage potential cyberattacks within ATS. Toward this end, this paper proposes QnGSA (hypercube driven graph self-attention intelligent vehicular intrusion detection model) in ATS, a novel intrusion detection model that helps to protect both the vehicles and the data they transmit, preventing disruptions to services, theft of sensitive information, and potential harm to passengers or cargo. QnGSA not only proposes a construction method of association graph by introducing hypercube and semi-supervised K-means++ clustering algorithm (QnSSKM). But also, QnGSA self-extracts the graph structural information of hypercube, and uses the graph self-attention mechanism to aggregate node features and obtain more accurate representation. Furthermore, this paper uses Graph Attention Network classifier to correlate the learned node representation with the fault category, and uses Softmax function to map the node representation to the probability distribution of different categories. The category with the highest probability is selected as the prediction label of the node, so as to realize the intelligent vehicular intrusion detection. Experiments results show that our proposed QnGSA method achieves the best results compared with state-of-the-art methods in terms of accuracy, macro precision/recall/F1.

Mobile Opportunistic Networks (MONs) often experience frequent interruptions in end-to-end connections, which increases the likelihood of message loss during delivery and makes users more susceptible to various cyber attacks. However, most currently proposed anonymous routing protocols are primarily designed for networks with stable connections, making it challenging to protect user identities in MONs. To address these challenges, we propose FLAG-POR (Forward Legal Anonymous Group Pairing-Onion Routing), a novel anonymous routing protocol specifically tailored to enhance message delivery anonymity and security in MONs. Specifically, we abstract the mobile opportunistic network as a contact graph. By introducing the concept of “groups” into the pairing-onion routing protocol, which encrypts messages and relay nodes layer by layer, we develop a novel group-based pairing-onion routing protocol. This protocol ensures message confidentiality and relay node anonymity, while also improving message forwarding rates, as any node within a group can potentially act as a relay. To ensure message authenticity, we employ the efficient SM2 signing algorithm to generate signatures for the message source. Furthermore, by incorporating parameters such as the public key validity period and master key validity period into the group pairing-onion routing protocol, we achieve forward security in message delivery. We conduct a thorough theoretical analysis of the protocol’s security and performance. The experimental results demonstrate that our FLAG-POR protocol outperforms baseline anonymous protocols in terms of delivery success rate, traceability rate, path anonymity, and node anonymity. Additionally, the FLAG-POR scheme effectively resists three potential threats to the routing system: collusion attack threat, node identification threat, and path identification threat, in any situation.

As the Augmented Intelligence of Things (AIoT) advances within vehicle-road coordination systems, challenges related to road traffic data transmission and processing are being increasingly addressed. However, this progress also brings significant risks of privacy data leakage. Federated learning (FL), a distributed machine learning paradigm, effectively safeguards client data privacy by allowing multiple participants to collaboratively train models while keeping their data localized. Despite its benefits, FL faces challenges, such as model parameter leakage and Byzantine attacks. To tackle these issues, this article introduces an anonymous and efficient FL framework for vehicle-road coordination systems (AEFL), designed to ensure a secure and reliable vehicle data transmission process. This architecture incorporates a novel group pairing onion routing protocol, which leverages pairing cryptography principles for hierarchical data encryption. During the routing process, relay group nodes decrypt the corresponding layer, ensuring both data confidentiality and node anonymity. Additionally, a sampling method is proposed to accurately identify Byzantine vehicle nodes, enhancing the precision of FL aggregation without compromising overall model performance. Experimental results show that AEFL outperforms the classic TOR anonymous routing protocol, achieving a 100% message delivery rate more quickly. Under the same conditions, the anonymity of the source node and the destination node improves by 3.9% and 1.9%, respectively. When half of the nodes are compromised, path anonymity can be increased by 24.8%. Furthermore, our framework excels in FL aggregation efficiency, with a Byzantine adversary detection accuracy of up to 99%.

The Augmented Intelligence of Things (AIoT) is an emerging technology that combines augmented intelligence with the Internet of Things (IoT) to facilitate advanced decision-making processes. In this paper, we focus on the detection of vehicle trajectory anomalies in a vehicle-road collaboration system by AIoT, aiming to improve the traffic safety and road operation efficiency. We transmit collaboration data collected by sensors to an IoT server, which enables the effective data analysis for vehicle trajectory information. We propose a self-supervised learning augmented intelligence algorithm to achieve precise and efficient detection of trajectory anomalies. First, we models the traffic road network as a topology graph. Subsequently, we sample the relevant subgraph contexts for each target node through a random walk algorithm. And the subgraphs with higher intimacy scores are selected as the contextual background to be input along with the target node. After that, the anomaly score of each target node is computed through the generative learning module and the contrastive learning module. To evaluate the effectiveness of our anomaly detection approach, we initially conduct pre-training of the model using four widely utilized graph machine learning datasets. The experimental results reveal that our approach surpasses previous methods in the accuracy of identifying graph anomaly nodes. In addition, we carry out our approach on two real traffic datasets with high accuracies of 86.47% and 85.2%, respectively. This result demonstrates the effectiveness of our proposed approach in detecting trajectory anomalies in real traffic scenarios.

Ensuring the stability and security of unmanned aerial vehicle (UAV) communication, especially during long-distance missions, is essential for safeguarding against potential attacks. Large-scale UAV communication faces challenges including eavesdropping threat, data tampering, replay threat and man-in-the-middle threat. We propose a security information transmission solution based on reinforcement learning and location confusion algorithm (RLPC-SIT) to achieve a secure data transmission between UAVs. First, we leverage the principles of reinforcement learning to identify the most stable transmission routes. Secondly, we employ location confusion techniques to blur each location of the transmitting UAV with respect to other UAVs. Furthermore, we utilize the concept of message authentication to encrypt the transmitted data, thus making it inaccessible to malicious nodes and preventing forgery. The results of our theoretical analysis and simulation-based experiments indicate that our approach outperforms other security schemes.

Digital healthcare services have become an integral part of our lives. There is an increasing number of healthcare professionals and patients using medical wearables for diagnosis and treatment, which simplifies and improves the diagnostic and therapeutic process. However, inappropriate use of medical data may result in the disclosure of private patient information. For protecting patients' privacy when using medical wearables, we propose a new blockchain-based data access security scheme. Specifically, the elliptic curve encryption algorithm and zero-knowledge authentication method are used to authenticate the identity of patients and doctors in the blockchain network. Furthermore, we develop a smart recommendation method based on deep reinforcement learning to recommend appropriate doctors for patients. Next, patients allow recommended doctors to access their medical data, and smart contracts specifically designed for secure data access to medical wearables will regulate subsequent data access. The security analysis and experimental results demonstrate that the proposed scheme can effectively protect patients' privacy during treatment through secure authentication and data access for medical wearables.

Since its inception, blockchain technology has found wide-ranging applications in various fields including agriculture, energy, and so on, owing to its immutable and decentralized nature. However, existing blockchains encounter significant challenges in scenarios that demand efficient retrieval of big data. This is primarily because current blockchains cannot directly store and process diverse types of rich media information. Additionally, the semantic relationships between data within the blockchains are weak, complicating the categorization and retrieval of data and transactions. Moreover, the scalability of current blockchains is limited, with the capacity of full nodes continually increasing. Although some semantic-based blockchain solutions that combine off-chain scalability have been proposed, they are limited in effectiveness and applications. To address these issues, this paper introduces a brand-new blockchain sharding technique called Semantic Sharding, which enhances blockchain scalability through a hybrid on/off-chain approach. Building on this, we propose a semantic sharding blockchain architecture, SemantiChain, which enables the on-chain storage and retrieval of transaction semantic features. Furthermore, through the Po2RW consensus protocol, we balance the scalability and security of SemantiChain. Security analysis proves that SemantiChain can resist security risks such as man-in-the-middle attacks, malicious node attacks and on/off-chain data inconsistency. Experimental results demonstrate that SemantiChain can reduce search time and memory usage by at least 32.29% and 77.97% respectively under the same retrieval performance, compared to mainstream approximate nearest neighbour retrieval algorithms. Furthermore, compared to the SOTA semantic blockchain, SemantiChain achieves a retrieval performance improvement of at least 45.88% and reduces retrieval memory usage by 95.76%.

Permissioned blockchain frameworks typically employ efficient Byzantine fault-tolerant consensus protocols, making them appealing for the deployment of fast transaction applications among a large number of mutually distrustful participants. However, existing permissioned blockchain frameworks typically use sequential serial workflows to invoke the consensus protocol and execute transactions for the application, resulting in significantly lower performance for these applications when deployed in traditional systems. Therefore, a new permissioned blockchain framework is needed to improve transaction processing efficiency and enhance system performance for practical blockchain technology applications. We propose IHFBF (Improved Hyperledger Fabric Blockchain Framework), an improved permissioned blockchain framework that employs a predictive transaction sorting method by selecting a node within the consensus nodes to act as a sorter. This enables parallel execution of the consensus protocol and transactions, resulting in improved overall system performance. However, if the sorter is a malicious node, it can severely impact system performance. To address this, IHFBF uses a view-change method based on a deny-list approach, which effectively guides all participants and replaces or denies malicious participants. Compared to other three fast permissioned blockchain frameworks, IHFBF’s parallel workflow framework reduces latency and exhibits better throughput in the presence of malicious participants, resulting in efficient system performance.

The issues of privacy and bias in datasets are rapidly becoming important challenges that the computer vision field needs to address. So far, there has been little attention paid to solutions for protecting the privacy of new datasets. In our work, we explored a object detection solution on the WIDER FACE dataset by anonymizing the dataset using face synthesis and enhancing the WIDER FACE dataset by balancing facial features along the dimensions of gender and skin color. Using both the original dataset and our enhanced dataset to train the target detection model, our target detection results show that our model can maintain detection performance while preserving privacy and partially balancing bias.

The traditional healthcare is overwhelmed by the processing and storage of massive medical data. The emergence and gradual maturation of Internet-of-Things (IoT) technologies bring the traditional healthcare an excellent opportunity to evolve into the IoT-enabled healthcare of massive data storage and extraordinary data processing capability. However, in IoT-enabled healthcare, sensitive medical data are subject to both privacy leakage and data tampering caused by unauthorized users. In this article, an attribute-based secure access control mechanism, coined (SACM), is proposed for IoT-Health utilizing the federated deep learning (FDL). Specifically, we manage to discover the relationship between users’ social attributes and their trusts, which is the trustworthiness of users rely on their social influences. By applying graph convolutional networks to the social graph with the susceptible–infected–recovered model-based loss function, users’ influences are obtained and then are transformed to their trusts. For each occupation, users’ trusts allow them to access specific medical data only if their trusts are higher than the corresponding threshold. Then, the FDL is applied to obtain the optimal threshold and relevant access control parameters for the improvement of access control accuracy and the enhancement of privacy preservation. The experimental results show that the proposed SACM achieves accurate access control in IoT-enabled healthcare with high data integrity and low privacy leakage.

Artificial Intelligence of Things (AIoT), as a fusion of artificial intelligence (AI) and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest (iforest), named D2MIF, for the federated learning-empowered AIoT. In D2MIF, an iforest is constructed to compute the malicious score for each model uploaded by the corresponding participant, and then, the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public data sets Mnist and Fashion_Mnist. The experimental results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning-empowered AIoT.

The deep integration of the Internet of Things (IoT) and the medical industry has given birth to the Internet of Medical Things (IoMT). In IoMT, physicians treat a patient's disease by analyzing patient data collected through mobile devices with the assistance of an artificial intelligence (AI)-empowered systems. However, the traditional AI technologies may lead to the leakage of patient privacy data due to its own design flaws. As a privacy-preserving federated learning (FL) can generate a global disease diagnosis model through multiparty collaboration. However, FL is still unable to resist inference attacks. In this article, to address such problems, we propose a privacy-enhanced disease diagnosis mechanism using FL for IoMT. Specifically, we first reconstruct medical data through a variational autoencoder and add differential privacy noise to it to resist inference attacks. These data are then used to train local disease diagnosis models, thereby preserving patients' privacy. Furthermore, to encourage participation in FL, we propose an incentive mechanism to provide corresponding rewards to participants. Experiments are conducted on the arrhythmia database Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH). The experimental results show that the proposed mechanism reduces the probability of reconstructing patient medical data while ensuring high-precision heart disease diagnosis.

The continuous development of Healthcare 4.0 has brought great convenience to people. Through the Internet of Things technology, doctors can analyze patients’ health data and make timely diagnosis. However, behind the high efficiency, the mobile crowdsensing technology used for data transmission still has the risk of leaking the privacy of task and patient information. To this end, this article proposes a privacy-enhanced multi-area task assignment strategy, named PMTA. Specifically, we use deep differential privacy to add noise to patient data, and then put the noise-added dataset into a deep Q-network for training, combined with a spectral clustering algorithm, to obtain an optimal classification strategy. Further, in order to address the problem of data silos, we adopt federated learning to jointly train the classification models of different hospitals to obtain a global model and realize data sharing among different hospitals. Finally, we use the optimal classification of patients for task deployment on the blockchain, and limit patients to only apply for tasks of the corresponding level through the smart contract technology, so as to protect task privacy. Experimental results show that our strategy can not only effectively protect task and patient privacy, but also achieve better system performance.

The convergence of Maritime Transportation Systems (MTS) and Internet of Things (IoT) has led to the promising IoT-empowered MTS (IoT-MTS). However, abnormal trajectories of maritime transportation ships can have highly negative impacts on the management of IoT-MTS. Therefore, anomaly detection of trajectories is important for the successful deployment of IoT-MTS. In this paper, we propose a Transfer Learning based Trajectory Anomaly Detection strategy, named TLTAD, for IoT-MTS. Specifically, a variational autoencoder is used to discover the potential connections between each dimension of the normal trajectory, while a graph variational autoencoder is used to explore the spatial similarity between normal trajectories. Based on internal connection of trajectories, a deep reinforcement learning algorithm, Twin Delayed Deep Deterministic policy gradient (TD3), is employed to train the trajectory anomaly detection model. To reduce the model training time, transfer learning is used to migrate the trained anomaly detection model between different regions of an ocean area or between similar ocean areas. Moreover, an efficient data transformation module is designed to improve the efficiency of model transfer. The experiments were conducted on a real-world automatic identification system (AIS) dataset. The results indicate that the proposed TLTAD can provide accurate anomaly detection on ships’ trajectories in IoT-MTS with reduced model training times.

The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.

The vigorous development of positioning technology and ubiquitous computing has spawned trajectory big data. By analyzing and processing the trajectory big data in the form of data streams in a timely and effective manner, anomalies hidden in the trajectory data can be found, thus serving urban planning, traffic management, safety control and other applications. Limited by the inherent uncertainty, infinity, time-varying evolution, sparsity and skewed distribution of trajectory big data, traditional anomaly detection techniques cannot be directly applied to anomaly detection in trajectory big data. To solve this problem, we propose a hierarchical trajectory anomaly detection scheme for Intelligent Transportation Systems (ITS) using both machine learning and blockchain technologies. To be specific, a hierarchical federated learning strategy is proposed to improve the generalization ability of the global trajectory anomaly detection model by secondary fusion of the multi-area trajectory anomaly detection model. Then, by integrating blockchain and federated learning, the iterative exchange and fusion of the global trajectory anomaly detection model can be realized by means of on-chain and off-chain coordinated data access. Experiments show that the proposed scheme can improve the generalization ability of the trajectory anomaly detection model in different areas, while ensuring its reliability.

Due to the privacy breach risks and data aggregation of traditional centralized machine learning (ML) approaches, applications, data and computing power are being pushed from centralized data centers to network edge nodes. Federated Learning (FL) is an emerging privacy-preserving distributed ML paradigm suitable for edge network applications, which is able to address the above two issues of traditional ML. However, the current FL methods cannot flexibly deal with the challenges of model personalization and communication overhead in the network applications. Inspired by the mixture of global and local models, we proposed a Communication-Efficient Personalized Federated Meta-Learning algorithm to obtain a novel personalized model by introducing the personalization parameter. We can improve model accuracy and accelerate its convergence by adjusting the size of the personalized parameter. Further, the local model to be uploaded is transformed into the latent space through autoencoder, thereby reducing the amount of communication data, and further reducing communication overhead. And local and task-global differential privacy are applied to provide privacy protection for model generation. Simulation experiments demonstrate that our method can obtain better personalized models at a lower communication overhead for edge network applications, while compared with several other algorithms.

The Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, in this paper, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning (DRL) algorithm. Since local data sets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios.

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a blockchain-based secure data aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers [i.e., mobile data collectors (MDCs)] to search and accept tasks, the block header is intergraded with a security label including task security level (SL) and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed to design energy-efficient MDC routes under the constrains that the SLs of MDCs should be higher than the SLs of data aggregation tasks. Simulation results indicate that 1) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency and 2) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.

The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.

With the development of smart mobile terminals and mobile communication technologies, Mobile Edge Computing (MEC) has been applied to a variety of fields. However, MEC also brings new data security threats including the data access threat. To solve the cross-domain access control problem in MEC, this paper proposes a cross-domain access control protocol, named CDAC. In CDAC, a new user reputation evaluation strategy is proposed, which dynamically evaluates the comprehensive reputation of users based on different access behaviors of users, so that gateway nodes can evaluate user cross-domain requests. Meanwhile, different priorities are assigned according to user security levels to encourage users to regulate access behaviors to improve their reputations. Then, different gateway nodes implement cross-domain access control for users. The experiment results show that the proposed CDAC can provide efficient cross-domain access controls and achieve excellent system performances.

In the process of sharing students’ credit information across schools and departments, there are some problems such as tampering and leaking of students’ credit information.In this paper, combined with the characteristics of blockchain traceability and difficult to tamper, a credit information access control method based on blockchain is proposed, which not only protects students’ privacy, but also realizes the cross school access control of students’ credit information.This paper designs a multi blockchain architecture that combines consortium blockchain and private blockchain of colleges and universities. It stores credit information summary on the blockchain and original records off the blockchain to relieve the storage pressure of blockchain; Then, the multi authorization attribute encryption technology is used to set the access policy for fine-grained access control.Finally, the simulation results show that the scheme can achieve fine-grained access control of students’ credit information while protecting students’ privacy.

The emergence of the Internet of Medical Things (IoMT) brings a huge impact on current medical system in the detection and prevention of medical diseases, as well as the sharing and analysis of medical data. To efficiently collect medical data for disease prevention, the mobile crowdsensing (MCS) is employed. However, the exposure of sensitive information about users and crowdsensing tasks might cause serious privacy leakage in MCS. To solve this problem, in this paper, a Privacy-enhanced Mobile Crowdsensing strategy utilizing Blockchain technology, named PMCB, is proposed. Specifically, we propose to classify the users by spectral clustering based on the social network generated by the social attributes of users. In this way, both crowdsensing tasks and participating users are classified such that task receivers are restricted to receive specific crowdsensing tasks. Furthermore, the blockchain is used to store crowdsensing tasks and smart contract is used for access control. Experiment results show that PMCB can achieve efficient privacy protection in mobile crowdsensing with high system throughput and low transaction latency.

With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people's willingness to contribute data in IoMT. To address these challenges, this article proposes a new privacy-enhanced data fusion strategy (PDFS). The proposed PDFS consists of four important components, i.e., sensitive task classification, task completion assessment, incentive mechanism-based task contract design, and homomorphic encryption-based data fusion. The extensive simulation experiments demonstrate that PDFS can achieve high task classification accuracy, task completion rate, task data reliability and task participation rate, and low average error rate, while improving the privacy protection for data fusion under COVID-19 application environments based on IoMT.

Mobile-edge crowdsensing is capable of providing a large amount of data via pervasive mobile terminals for Industrial Internet of Things (IIoT). However, the generated data often contain users' sensitive information, which suggests the significance of privacy preserving in data aggregation and analysis for IIoT. Privacy preserving in mobile-edge crowdsensing have conflicting objectives, i.e., the edge fusion center (FC) requires data of better quality for data fusion with higher accuracy whereas participatory users (PUs) desire better privacy preserving by larger noise injection. Therefore, how to select proper noises to achieve the tradeoff between accuracy and privacy is a challenging problem. In addition, FC is subject to data tempering due to the lack of data reliability validations and incentive mechanisms. To tackle these problems, we propose a novel privacy-preserving mobile-edge crowdsensing strategy (PPCS) for IIoT. Specifically, PPCS provides a Kullback-Leibler privacy-preserving data aggregation using a reputation-based incentive mechanism. On the other hand, PPCS offers hypothesis test-based data reliability validation and PU's reputation update, which collaborate to ease the impact of tampered data. Meanwhile, a reinforcement learning algorithm, the expected Sarsa, is applied to obtain the optimal test threshold. Theoretical analysis and experimental results show that PPCS is an energy-efficient strategy and the data provided by PPCS has a better aggregation accuracy than certain baseline strategies.

As an emerging technology, the industrial Internet of Things (IIoT) can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. In IIoT, the improvement and progress of industrial production and applications are inseparable from data fusion, a process that realizes the collection, analysis, and processing of the massive IoT data generated by industrial equipment and applications. IIot demands a real-time, effective, and privacy-preserving data fusion process. However, the existing works need to train different learning models for data analysis, which cannot meet real-time requirements in IIoT. Meanwhile, the lack of defense against internal attacks and the difficulty to balance system performance and privacy protection hinder the effectiveness and privacy protection in the data fusion process. To solve the abovementioned problems, in this article, we propose a new transfer learning-based secure data fusion strategy (TSDF) for IIoT. The proposed TSDF consists of three parts, guidance based deep deterministic policy gradient (GDDPG) algorithm for task classification, transfer learning based GDDPG for grouping of task receivers, and a multiblockchain mechanism for privacy preservation. The experiment results show that TSDF can achieve high system throughput and low latency, providing privacy preservation in data fusion under various IIoT application environments.

Industrial Internet of Things (IIoT) is ushering in huge development opportunities in the era of Industry 4.0. However, there are significant data security and privacy challenges during automatic and real-time data collection, monitoring for industrial applications in IIoT. Data security and privacy in IIoT applications are closely related to the reliability of users, which is determined by user authentication that have been widely used as an effective approach. However, the existing user authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability with the rapid growth of the number of users and the diversity of user categories. To solve the aforementioned issues, this article proposes a novel Authentication mechanism based on Transfer Learning empowered Blockchain, coined ATLB. In ATLB, blockchains are applied to achieve the privacy preservation for industrial applications. In addition, by introducing the transfer learning based authentication mechanism, trustworthy blockchains are built such that the privacy preservation for industrial applications is further enhanced. Specifically, ATLB first employs a guiding deep deterministic policy gradient algorithm to train the user authentication model of a specific region, which is then transferred locally for foreign user authentication or cross-regionally for another region's user authentication such that the model training time is significantly reduced. Experimental results show that the proposed ATLB not only provides accurate authentications for IIoT applications but also achieves high throughput and low latency.

Cognitive radio networks (CRNs) are vulnerable to spoofing attacks due to their wireless and cognitive nature. Since the traditional cryptographic authentication can hardly prevent such attacks in CRNs, the physical‐layer authentication has been investigated for recent years. To achieve a light‐weight physical‐layer authentication, a rollout partially observable Markov decision process‐based algorithm, named RoPOMDP, is proposed in this study. In general, RoPOMDP formulates the physical‐layer authentication as a zero‐sum game, based on which a hypothesis test upon channel vectors is developed. That allows us to design the gains for both spoofers and receivers based on Bayesian risks for the game, in which the spoofing attack probability is predicted by a non‐linear function approximation utilising v‐support vector regression. Then, a RoPOMDP is employed to estimate the optimal threshold for the test statistic such that spoofing attacks can be detected. The theoretical analysis and simulations indicate that: (i) RoPOMDP improves the spoofing detection accuracy; (ii) as a light‐weight algorithm, the complexity of RoPOMDP is lower than contemporary ones.

Various services such as location-based service (LBS) allow mass collection of spatio-temporal data because the ubiquity of cheap embedded sensors on smart phones. Therefore, the individual privacy-preserving is receiving increasing attention during the data publication. However, the inherent inaccuracy of data acquisition equipments, sampling error and low sampling rate may lead to uncertainty. In this paper, we propose a privacy-preserving approach for trajectory publication with considering the uncertainty in trajectory. The correlation between two trajectories are computed according to the temporal overlap similarity, the trajectory direction similarity and the distance between trajectories with uncertainty. Then a greedy algorithm is proposed to achieve k-anonymity based on graph partition. The analysis and experiment evaluations based on the GeoLife trajectory data set show that significant privacy and QoS benefits can be achieved.