In federated learning (FL), the aggregation server is typically assumed to be honest-but-curious–it faithfully follows the aggregation protocol but attempts to infer private information as much as possible. Adversaries may manipulate a dishonest server to maximize illegal benefits, such as reducing the number of aggregation rounds to save computation or fabricating aggregation results to affect model updates and performance. To ensure the security of FL aggregation, it is crucial to verify the correctness of the server-side aggregation results and maintain the consistency of model distribution during the FL process. To address the issues of high overhead and insufficient integrity in verifying dishonest servers, this paper proposes a lightweight privacy-preserving verifiable federated learning framework, named LPV-FL. This framework adopts a linear masking mechanism to protect gradient privacy, introduces a local verification method based on linear homomorphic hashing to improve verification efficiency, and incorporates blockchain to ensure the consistency and auditability of the global model. Experimental results show that LPV-FL reduces computation and communication overhead while guaranteeing integrity and verification performance. Compared with the baseline, it achieves a 63.18% reduction in verification time and a 98.73% reduction in communication overhead.

ABSTRACT Background With the increasing integration of Artificial Intelligence (AI) and Internet of Things (IoT), the dissemination of multimodal data is undergoing revolutionary changes. To mitigate the societal risks posed by the rapid spread of malicious multimodal data, such as hateful memes, it is crucial to develop effective detection methods for such data. Existing detection models often struggle with data quality issues and lack interpretability, limiting their effectiveness in content moderation tasks. Aims This paper aims to propose an explainable hateful meme detection model by uncertainty‐aware dynamic fusion. The goal is to enhance both generalization performance and interpretability, addressing the limitations of conventional static fusion methods and existing algorithms for hateful meme detection. Materials & Methods To mitigate the societal risks posed by the rapid spread of malicious multimodal data, such as hateful memes, it is crucial to develop effective detection methods for such data. However, existing algorithms for hateful meme detection frequently overlook the data quality and the interpretability of model. To adress these challenges, this paper proposes Hate‐UDF, an explainable hateful meme detection model with uncertainty‐aware dynamic fusion, providing both high generalization ability and interpretability. This method dynamically evaluates the uncertainty of different modalities, obtains dynamic weights, and utilizes them to weight the feature values for fusion, thereby obtaining a uncertainty‐aware dynamic fusion method with provable upper bounds on generalization error. Furthermore, an analysis of the dynamic weights can explain the modality on which the model primarily relies for detection, thereby providing a method that is both explainable and reliable. Results We compare the performance of Hate‐UDF with three general models and three State of the Art (SOTA) models in the field of hateful meme detection on the Facebook Hateful Memes (FHM) and the Multimedia Automatic Misogyny Identification (MAMI) datasets. Hate‐UDF achieved state‐of‐the‐art performance, surpassing existing models on both datasets. Specifically, it improved accuracy and AUC by 7.56% and 2.8% on FHM and by 3.34% and 0.17% on MAMI compared with the current SOTA model, respectively. Additionally, we demonstrate that the visual modality is more important than the textual modality in the hateful meme detection model, and we explain the primary reason behind this by visualization. Discussion The model dynamically adapts to modality quality, enhancing reliability and reducing the risk of misclassification. Its interpretability, achieved through visualizations of modality and feature attributions, provides valuable insights for content moderation systems and highlights the importance of image modality in detecting hateful meme. While Hate‐UDF provides an explainable and reliable method for detecting hateful memes, it may still learn biases from the training data, potentially leading to the over‐detection of content from certain groups or communities. Future research must focus on improving the fairness and ethical responsibilities of the model's decisions. Conclusion This paper introduces the model of Hate‐UDF, a dynamic fusion method based on uncertainty, designed to improve multimodal fusion issues in existing hateful meme detection models. The model determines the reliability of different modal information by assessing their uncertainty and generates dynamic weights accordingly. By comparing these weights, the model can identify which modality is most influential in detecting malicious content. Therefore, the Hate‐UDF model not only has interpretability but also its generalization performance has been validated.

Model prediction based on machine learning is provided as a service in cloud environments, but how to verify that the model prediction service is entirely conducted becomes a critical challenge. Although zero-knowledge proof techniques potentially solve the integrity verification problem when applied to the prediction integrity of massive privacy-preserving Convolutional Neural Networks (CNNs), the significant proof burden results in low practicality. In this research, we present psvCNN (parallel splitting zero-knowledge technique for integrity verification). The psvCNN scheme effectively improves the utilization of computational resources in CNN prediction integrity, proving by an independent splitting design. Through a convolutional kernel-based model splitting design and an underlying zero-knowledge succinct non-interactive knowledge argument, our psvCNN develops parallelizable zero-knowledge proof circuits for CNN prediction. Furthermore, psvCNN presents an updated Freivalds algorithm for a faster integrity verification process. Experiments show that psvCNN is practical and efficient in terms of proof time and storage, generating a prediction integrity proof with a proof size of 1.2MB in 7.65s for the structurally complicated CNN model VGG16. psvCNN is 3765 times faster than the latest zk-SNARK-based non-interactive method vCNN, and 12 times faster than the latest sumcheck-based interactive technique zkCNN in terms of proving time.

The integrity of cloud-based convolutional neural network (CNN) prediction services can be jeopardized by a malicious cloud server. Although zero-knowledge proof approaches can be used to verify integrity, they are difficult to use for larger CNN models like LeNet-5 and VGG16, due to the large cost (in terms of time and storage) of generating a proof. This paper proposes ValidCNN, which can efficiently generate integrity proofs based zk-SNARK. At the heart of ValidCNN, it is a novel usage of Freivald's concepts for circuit construction, and a more efficient way for verifying matrix multiplication. Our experimental results demonstrate that VaildCNN significantly outperforms the state-of-the-art approaches that are based on zk-SNARK. For example, compared with ZEN, VaildCNN achieves a 12-fold improvement in time and a 31-fold improvement in storage. Compared with vCNN, VaildCNN achieves a 195-fold and 279-fold improvement in time and storage respectively.

Satellite communication contributes tremendously to the Industrial Internet of Things (IIoT) with telecommunication efficiency and data accessibility at global-scale coverage. However, realizing proactive defense for satellite communication remains a challenge. In order to address such an issue, we first explore state-of-the-art proactive defense methods and, following next, a step forward on proposing an end hopping scheme based on fixed hopping timeslot and strict time synchronization strategy by utilizing moving target defense (MTD). In addition, we worked on a Proof of Concept (PoC) to evaluate the scheme’s theoretical protection performance for Distributed Denial of Service (DDoS). Experimental evaluation and analysis of the proposed scheme show that it is efficient and secure, as seen when the attack rate is 100 times/s, the response time of the hopping state is 69.98% shorter than that of the normal state, and when the attack rate is 1000 times/s, the response time of the hopping state is 90.15% shorter.

Summary The risk of sharing data in cloud computing has gathered increasing attention. After the owner of some confidential data outsources the data to cloud storage services and shares it with others, the data owner lost the control to the data to a large extent. To achieve data sharing while keeping data confidentiality, attribute‐based encryption (ABE) can be employed by cloud storage services. However, ABE can only guarantee that outsourced data on the cloud is decrypted by attribute‐satisfying users but cannot restrict data from being accessed by dishonest users whose attributes also satisfy the access‐control policy. It is impossible for the data owner to control the shared data after it has been decrypted by dishonest users, especially when a set of attribute‐satisfying dishonest users may collude. To address this concern, we propose a traceable data sharing scheme called TraceChain. In TraceChain, data is encrypted over a new CP‐ABE scheme called E‐CP‐ABE. Furthermore, the system parameters for generating the private key in E‐CP‐ABE are uploaded to the private blockchain and transactions are performed on the chain. The data owner can obtain the identity of users by monitoring system parameters simultaneously and control data sharing on the blockchain. To prove the security of our scheme, the security analysis is given in this paper. Meanwhile, experimental results also show that our system is viable and efficient.

Data credibility plays a key role in facilitating evidence-based decision making in organizations and governments (e.g., policy making). One of the key data sources is the Internet of Things (IoT) devices and systems, say within a fog environment. However, the increasing complexity and interconnectivity of such IoT and fog environments can result in security vulnerabilities (e.g., due to implementation errors or flaws in the underpinning devices or systems), which can be exploited to compromise the credibility of the data. Therefore, in this article, we propose a secure Blockchain-based scheme to guarantee the credibility of nodes and data and ensure data transmission security in the fog environment. We then demonstrate the feasibility of the proposed scheme using experiments.

The cloud computing, as a more distributed and more efficient paradigm with better performance, has played an important role in many fields. The cloud environment under the IoT has also assumed many roles such as the storage, the processor, the service provider and so on. However, the complex deployment and usage environment of the IoT brings new security risks to cloud computing. In response to this situation, this poster preliminarily designed a security scheme for the cloud environment of IoT. Based on the identity verification algorithms and blockchain technology, the credibility of data stored in the cloud can be ensured, while the security of data transmission from the cloud to data consumers is achieved.

The massive redundant data storage and communication in network 4.0 environments have issues of low integrity, high cost, and easy tampering. To address these issues, in this article, a secure data storage and recovery scheme in the blockchain-based network is proposed by improving the decentration, tampering-proof, real-time monitoring, and management of storage systems, as such design supports the dynamic storage, fast repair, and update of distributed data in the data storage system of industrial nodes. A local regenerative code technology is used to repair and store data between failed nodes while ensuring the privacy of user data. That is, as the data stored are found to be damaged, multiple local repair groups constructed by vector code can simultaneously yet efficiently repair multiple distributed data storage nodes. Based on the unique chain storage structure, such as data consensus mechanism and smart contract, the storage structure of blockchain distributed coding not only quickly repair the nearby local regenerative codes in the blockchain but also reduce the resource overhead in the data storage process of industrial nodes. Experimental results show that the proposed scheme improves the repair rate of multinode data by 9% and data storage rate increased by 8.6%, indicating to be promising with good security and real-time performance.