Cross-domain federated learning aims to collaboratively train a generalized model across clients with heterogeneous domain distributions without sharing data. Existing methods typically leverage prototypes to align intermediate representations among local models and enhance collaborative knowledge sharing, constructed either by directly aggregating class-center features across clients or by performing clustering to improve diversity. However, their performance is limited by the suboptimal ability to balance the learning of generalized and domain-specific features. To address this issue, this paper presents a novel dual-prototype guided FL framework named FedOrthrus, which decomposes the prototype into two components: i) the generalized prototype to capture cross-client domain-invariant features, and ii) the domain-specific prototype to extract the specific features of each domain. Specifically, the cloud server aggregates generalized prototypes to capture shared semantics across clients, thereby guiding each client to learn domain-invariant representations. Meanwhile, a clustering strategy is employed to adaptively construct domain-specific prototypes, ensuring that the representational capacity allocated to each domain is balanced according to its semantic complexity. Moreover, FedOrthrus employs a distribution-aware prototype construction scheme to dynamically assign the size of each part of prototypes, which enhances adaptability to different levels of domain heterogeneity. The experimental results on three datasets demonstrate that our FedOrthrus can achieve up to 14.56% and 3.96% accuracy improvement compared to traditional and state-of-the-art prototype-based FL methods. Our code is available at https://github.com/AAuZZ/FedOrthrus.

With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework has shown promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges, such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous FL approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.

This article aims to explore a reliable target recognition technique for autonomous aerial vehicles (AAV) clusters, and proposes a lightweight collaborative target recognition methods based on multiple viewpoints. In the proposed method, the AAVs are divided into the leaf node AAVs and the head node AAVs. Leaf node AAVs are used for multiview image acquisition and image feature extraction by utilizing a lightweight feature extraction model. The head node AAVs realize efficient feature fusion from the collected image features by using graph convolutional network and graph coarsening techniques. Based on the above lightweight technologies, the proposed method can realize the efficient and accurate target recognition and reduce the demand for limited computing resources and communication resources in AAV clusters. Experimental results show that, compared with existing multiview object recognition method, the proposed method has less computing cost and communication overhead while ensuring reliable recognition accuracy.

With the emergence of edge computing, there is a growing need for advanced technologies capable of real-time, efficient processing of complex data on edge devices, particularly in mobile health systems handling pathological images. On edge computing devices, the lightweighting of models and reduction of computational requirements not only save resources but also increase inference speed. Although many lightweight models and methods have been proposed in recent years, they still face many common challenges. This article introduces a novel convolution operation, Dynamic Scalable Convolution (DSC), which optimizes computational resources and accelerates inference on edge computing devices. DSC is shown to outperform traditional convolution methods in terms of parameter efficiency, computational speed, and overall performance, through comparative analyses in computer vision tasks like image classification and semantic segmentation. Experimental results demonstrate the significant potential of DSC in enhancing deep neural networks, particularly for edge computing applications in smart devices and remote healthcare, where it addresses the challenge of limited resources by reducing computational demands and improving inference speed. By integrating advanced convolution technology and edge computing applications, DSC offers a promising approach to support the rapidly developing mobile health field, especially in enhancing remote healthcare delivery through mobile multimedia communication.

Graph Neural Networks (GNNs) are highly susceptible to numerous adversarial attacks, among which the backdoor attack is one of the toughest to deal with due to the fact that it can lead to misclassification of the model. Similar to Deep Neural Networks (DNNs), backdoor attacks in GNNs work by an attacker changing a portion of the graph data with a hidden trigger and modifying their labels to target labels, which induces the model to learn the trigger feature during its training phase. Although recent defense techniques have emerged, approaches based on explainability and data isolation often fail to detect malicious samples with covert triggers, while discrepancy learning methods tend to degrade performance by removing useful features. To overcome these limitations, we propose a novel backdoor defense method, namedGraphCleanse, on GNNs that can effectively eliminate the possible backdoor features during the training process. Specifically,GraphCleansecan easily break the strong correlation between backdoor features and target labels based on graph contrastive training. To further improve the model accuracy, we present a mutual information maximization method to learn the important feature information in the labeled credible samples and unlabeled suspicious samples by clustering the features obtained from the graph contrastive encoder. Compared with the potential solutions, such as randomized smoothing,GraphCleanseeffectively avoids the negative influence of backdoored samples while maintaining a high model performance. Extensive experimental evaluations on four benchmark datasets demonstrate thatGraphCleansecan reduce the attack success rate to 10% with less performance degradation (within 7%).

The demand for mobile terminals to participate in data services is increasingly vital. The General Data Protection Regulation (GDPR) has established several principled requirements for data services. Existing studies focusing on data service put emphasis on data privacy and accessibility. However, they face challenges in achieving data forgetability and portability on mobile devices under GDPR and lack consideration of usage control. In this article, we propose ADSS, an app-level data service scheme for mobile devices that can beavailable-but-invisibleand guarantee fine-grained usage control. ADSS addresses the challenges by executing the logic of data usage in the Trusted Execution Environment (TEE) and managing the TEE states (i.e., data usage states) in the blockchain smart contracts. It not only satisfies the requirements of GDPR, ensuring strong security and confidentiality guarantees, but also enables the functionality of “pay-per-use”. We implement a prototype of the ADSS framework based on ARM Trustzone and conduct experimental evaluations. The results demonstrate that our scheme brings high efficiency compared with other data service schemes and exhibits feasibility on mobile-grade devices.

In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology-Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP’s efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.

In this paper, we propose a novel Multi-keyword Fuzzy Symmetric Searchable Encryption (SSE) with patterns hidden, namely MFSSE. In MFSSE, the search trapdoor can be modified differently each time even if the keywords are the same when performing multi-keyword search to prevent the leakage of search patterns. Moreover, MFSSE modifies the search trapdoor by introducing random false negative and false positive errors to resist access pattern leakage. Furthermore, MFSSE utilizes efficient cryptographic algorithms (e.g., Locality-Sensitive Hashing) and lightweight operations (such as, integer addition, matrix multiplication, etc.) to minimize computational and communication, and storage overheads on mobile devices while meeting security and functional requirements. Specifically, its query process requires only a single round of communication, in which, the communication cost is linearly related to the number of the documents in the database, and is independent of the total number of keywords and the number of queried keywords; its computational complexity for matching a document is$O(1)$; and it requires only a small amount of fixed local storage (i.e., secret key) to be suitable for mobile scenarios. The experimental results demonstrate that MFSSE can prevent the leakage of access patterns and search patterns, while keeping a low communication and computation overheads.

Most existing semantic segmentation methods primarily employ supervised learning with discriminative models. Although these methods are straightforward, they overlook the modeling of underlying data distributions. In this paper, we propose a novel medical image segmentation framework called Diff-SFCT based on Diffusion Model. We formulate semantic segmentation as a generative problem for segmentation masks, replacing the conventional pixel-wise discriminative learning with a latent prior learning process to produce more accurate segmentation results. Diff-SFCT employs a backbone network combining Convolutional Neural Network (CNN) and Transformer, and utilizes the local perception of CNN and the global information modeling capability of Transformer. In Diff-SFCT, we design a Semantic Encoder that effectively extracts fine-grained semantic features from real images. Meanwhile, we propose a novel Spatial-Frequency Cross Transformer (SFCT) framework, which can effectively model and interact the global features of the diffuse noise mask and the real semantic features, reducing the domain gap between the two and enhancing the model’s representational capacity. Additionally, to preserve spatial and frequency information in the diffusion model, we design a Spatial-Frequency Attention Module (SFAM) as part of the Convolutional Block. This module improves the model’s spatial and frequency perception abilities while incurring negligible computational overhead. Experimental results evince that our DiffSFCT substantially outperforms other segmentation methods, exhibiting remarkable performance across various medical image segmentation datasets.

Mitigating adversarial deep learning attacks remains challenging, partly because of the ease and low cost in carrying out such attacks. Therefore, in this paper, we focus on the understanding of universal adversarial example attack on image classification models. Specifically, we seek to understand the difference(s) between adversarial examples in two adversarial datasets (DAmageNet and PGD dataset) and clean examples in ImageNet learned by the classification model, and whether we can use such findings to resist adversarial example attacks. We also seek to determine if we can retrain a discriminator to discriminate whether the input image is an adversarial example, using adversarial training. We then design a number of experiments (e.g., class activation map (CAM) analysis, feature map analysis, feature maps/filters changing, adversarial training, and binary classification model) to help us determine whether the universal adversarial dataset can be successfully used to attack the classification model. This, in turn, contributes to a better understanding of adversarial defenses over pretrained classification model from an interpretation perspective. To the best of our knowledge, this work is one of the earliest works to systematically investigate the interpretation of universal adversarial example attack on image classification models, both visually and quantitatively.

As an emerging technology, industrial Internet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this article, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.

Resisting the adversarial sample attack on encrypted traffic is a challenging task in the Intelligent Transportation System. This paper focuses on the classification, adversarial samples attack and defense method for the encrypted traffic. To be more specific, the one-dimensional encrypted traffic data is firstly translated into the two-dimensional images for further utilization. Then different classification networks based on the deep learning algorithm are adopted to classify the encrypted traffic data. Moreover, various adversarial sample generation methods are employed to generate the adversarial sample to implement the attacking process on the classification network. Furthermore, the passive and active defense method are proposed to resist the adversarial sample attack: 1) the passive defense is used to denoise the perturbation in the adversarial sample and to restore to the original image; and 2) the active defense is used to resist the adversarial sample attack by leveraging the adversarial training method, which can improve the robustness of the classification network. We conduct the extensive experiments on the ISCXVPN2016 dataset to evaluate the effectiveness of classification, adversarial sample attacking and defending.

The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients' medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three data sets, namely, the Montgomery County chest X-ray data set, the Ultrasonic Brachial Plexus data set, and the BraTS18 data set. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.

Internet of Medical Things (IoMT) can connect many medical imaging equipment to the medical information network to facilitate the process of diagnosing and treating doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep-learning-based image encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the cycle-generative adversarial network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain. The target domain is regarded as “hidden factors” to guide the learning model for realizing the encryption. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve image decryption. In order to facilitate the data mining directly from the privacy-protected environment, a region of interest (ROI)-mining network is proposed to extract the interesting object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray data set. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.

The emerging of healthcare Industrial Internet of Things (HealthIIoT) cannot only facilitate high-quality care services for patients but also enable efficient telemedicine platform for healthcare practitioners. However, it faces several fundamental security and privacy challenges, such as secure fine-grained data delivery, privacy preserving keyword-based ciphertext retrieval, malicious key delegation, and efficiency of the system. To combat these issues, we propose a privacy-aware and traceable fine-grained system (PTFS) for secure data delivery in cloud-assisted HealthIIoT. Compared to the existing solutions that only implement some of the preceding features, the proposed solution enables secure fine-grained data delivery, privacy-preserving data retrieval, efficient encryption and decryption operations, and trace of malicious key delegation simultaneously. For security analysis, rigorous proofs of the proposed scheme are provided to prove its security. In addition, extensive simulations and experiments are conducted for performance evaluation, which demonstrate the feasibility and effectiveness of PTFS.

Key agreement is one the most essential steps when applying cryptographic techniques to secure device‐to‐device (D2D) communications. Recently, several PHY‐based solutions have been proposed by leveraging the channel gains as a common randomness source for key extraction in wireless networks. However, these schemes usually suffer a low rate of key generation and low entropy of generated key and rely on the mobility of devices. In this paper, a novel secret key extraction protocol is proposed by using interference in wireless D2D fading channel. It establishes symmetrical keys for two wireless devices by measuring channel gains and utilizing artificial jamming sent by the third party to change the measured value of channel gains. We give a theoretically reachable key rate of the proposed scheme from the viewpoint of the information theory. It shows that the proposed scheme can make hundred times performance gain than the existing approaches theoretically. Experimental results also demonstrate that the proposed scheme can achieve a secure key distribution with a higher key rate and key entropy compared with the existing schemes.

The wireless body area networks (WBANs) have emerged as a highly promising technology that allows patients’ demographics to be collected by tiny wearable and implantable sensors. These data can be used to analyze and diagnose to improve the healthcare quality of patients. However, security and privacy preserving of the collected data is a major challenge on resource-limited WBANs devices and the urgent need for fine-grained search and lightweight access. To resolve these issues, in this paper, we propose a lightweight fine-grained search over encrypted data in WBANs by employing ciphertext policy attribute based encryption and searchable encryption technologies, of which the proposed scheme can provide resource-constraint end users with fine-grained keyword search and lightweight access simultaneously. We also formally define its security and prove that it is secure against both chosen plaintext attack and chosen keyword attack. Finally, we make a performance evaluation to demonstrate that our scheme is much more efficient and practical than the other related schemes, which makes the scheme more suitable for the real-world applications.