With the rapid development of cryptocurrencies, the demand for cross-chain transactions (CCTx) has increased significantly. Although mainstream approaches, such as notary schemes, hash time-lock contracts (HTLC), and relay chains, can enable cross-chain asset transfers, they generally suffer from low efficiency and high cost issues. To address these challenges, this paper proposes a Notary Group-Based Cross-Chain Channel Protocol (NCCP) for secure and scalable interoperable payments, which integrates off-chain payment channels with a notary-based mechanism to achieve efficient and secure CCTx. The protocol facilitates multiple rounds of asset interactions off-chain and submits final settlement to the blockchain only upon channel closure, thereby significantly reducing on-chain overhead. To mitigate security challenges arising from blockchain isolation, a verifiable notary mechanism is designed to ensure consistency of channel states, accompanied by an incentive and penalty scheme to enhance system reliability. A prototype system is implemented on the Ethereum test networks. Security analysis and experimental results demonstrate that NCCP outperforms existing approaches in terms of both performance and security.

Thanks to the advent and rapid development of fog computing, the smart grid has made great progress. In fog-based smart grid, the fog node can maintain and handle the data for the smart meter which is resource-constraint, thus improving the performance. However, the security of the data (especially for malicious data reservation) is the primary concern of the smart meter. To resist the malicious outsourced data reservation in fog-based smart grid, we propose a provable deletion method in this article. In our method, we first improve the Merkle hash tree and design a novel tree named Merkle position index hash tree (MPIHT). For maintaining the same number of data blocks, MPIHT can reduce the height of the tree since it is able to store plenty of data blocks in every leaf. At the same time, the number of data blocks in every leaf would be changeably, thus supporting data deletion operation. Subsequently, we use MPIHT to propose a secure and provable deletion method for outsourced data. Our method can guarantee data integrity and achieve provable data deletion, requiring neither a trusted third party nor any complex calculations or protocols. Moreover, we demonstrate the security analysis to formally prove that our method can meet the expected requirements. Finally, we also implement our method to assess the performance. The assessment results disclose the efficiency advantages of our method over some existing methods.

The emergence of big data and artificial intelligence (AI) marks a significant milestone in technological advancement, impacting various sectors and transforming the essence of public and personal activities. Traditional machine learning, known as centralized learning, involves collecting data from multiple sources for the model development, which poses significant privacy risks. To address the conflict between the need for data sharing and the protection of privacy, federated learning (FL) has emerged as a promising solution. This approach allows for the continuous sharing of model updates between a central server and numerous local devices, fostering collaborative model development. However, it also brings about considerable communication costs and raises privacy concerns due to the potential for sensitive information leakage from the central server and local devices. To address these issues, we propose a lightweight and accuracy-lossless privacy-preserving FL scheme based on gradient clipping. The central server introduces noise to the global model to prevent clients from extracting valuable information, and then the local devices train these models using their data. To reduce the communication load, parameters with less impact on the model are removed using the Fisher information matrix. Additionally, to enhance privacy protection, the client-computed parameters are perturbed using the Diffie-Hellman key exchange method. Our experiments show that this approach greatly reduces the communication load for clients and the server, while effectively safeguarding client privacy and maintaining the model’s accuracy.

Forest fire detection and resource monitoring are the focus of forest resource protection, but they often suffer from internal attacks. Insider attackers try to get away with illegal activities such as illegal logging and encroachment on forest resources by manipulating data. Illegal activities pose a huge threat to forest resources, so defending against internal attacks is key to forest regulation systems. This paper presents a blockchain-based forest fire detection system (FFD) in a multi-party environment. FFD introduced the blockchain + database architecture for data storage and designed a hybrid encryption and decryption algorithm using CP-ABE and AES algorithms to realize data sharing and access control so as to ensure data security. The artificial intelligence technology is employed to identify fires, reducing the human factor in fires. Theoretical analysis and experimental data show that FFD has high performance and is suitable for forest fire detection and monitoring.

The development of the Internet of Medical Things has made online diagnostic systems an attractive application. However, the opacity and insufficient supervision of cloud computing can result in problems such as data leakage and unreliable outcomes in diagnostic systems. In order to solve these problems, this paper proposes a verifiable privacy-preserving online diagnosis scheme (VPOD) based on multiclass SVM. This solution uses CKKS leveled homomorphic encryption to build a secure inner product computation protocol and a secure decision function computation protocol to achieve privacy protection for multiclass SVM model parameters, patient medical data and diagnosis results. In addition, this article designs a verification mechanism for multiclass SVM model predictions to ensure the correctness of the results. Performance evaluation shows that VPOD is more functional, has low computational overhead for patients and hospitals, and has an accuracy of 98.64% on real data sets.

Federated Learning, as a distributed learning model, enables multiple clients to collaboratively train models while preserving data privacy. However, recent studies have highlighted a potential drawback: sharing gradient information could unintentionally lead to the exposure of private training data, allowing attackers to reconstruct this data from the shared gradients. To defend against this threat while maintaining high model accuracy, we propose a defensive method called Gradient Perturbation-based Dynamic Clipping (GPDC). This method mitigates the risk of gradient information leakage by introducing minor perturbations to the shared gradients and employing dynamic clipping techniques to preserve model accuracy. It combines two approaches: gradient perturbation and segmented clipping. Clients use an adaptive mechanism to dynamically trim gradients. After trimming, noise scales are adaptively added, with the noise determined by the clipping threshold and gradient changes. The effectiveness of the proposed defensive strategy was evaluated through experiments on the MNIST and CIFAR10 datasets. The results reveal that the GPDC method successfully resists DLG attacks while maintaining high model performance.

Edge computing serves as a potent solution for distributed learning tasks, offering benefits such as low latency, reduced network load, enhanced data security and privacy, adaptability to IoT development, and flexible deployment options. Introducing greater randomness and stability through the random selection of clients for communication in each iteration round enhances overall system performance. To solve the problem of high communication cost in traditional federated learning and model performance degradation in heterogeneous environments, we propose a lightweight decentralized federated learning framework, which uses soft threshold ternary quantization to compress local models. Soft threshold ternary quantization is a method to compress weight parameters into discrete values, which significantly reduces the complexity of parameter representation, thus saving storage space and communication costs. We devise an algorithm for selecting clients with similar data distributions to enhance model accuracy and expedite convergence in heterogeneous environments. Our framework ensures model performance while minimizing communication costs and safeguarding user privacy. Experiments show that compared with the existing ternary quantization federated learning method, the accuracy of the model we trained is improved.

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

With the development of Automatic Dependent Surveillance-Broadcast (ADS-B) technology in the aviation industry, a large amount of data are generated in ADS-B systems everyday. Cloud storage can satisfy the requirement of storing a large amount of ADS-B data well, however, the data stored on cloud server also raises problems about the security and confidentiality. This paper proposes a secure ADS-B data outsourcing scheme with integrity guarantee and multi-keyword retrieval (MRDP) to address these issues. In our MRDP, an index is generated for each piece of ADS-B data through Bloom filters to enable multi-keyword retrieval from clouds. Also, a unique label is produced for each piece of ADS-B data for integrity verification of query results. Our MRDP solution enjoys completeness property in that all query results with regard to the multiple keywords would be returned by the cloud server, otherwise it could be detected by the user. Security analysis indicated that our scheme offers integrity, completeness and privacy protection on outsourced ADS-B data, under the computational Diffie-Hellman (CDH) assumption and the discrete logarithm (DL) assumption. Theoretical and experimental analyses demonstrate the practicality of our proposed MRDP construction compared to existing solutions.

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

Payment channel networks (PCNs) are generally regarded as one of the most effective and promising scalability solutions for blockchain-based cryptocurrency systems, but suffer the issues of low success ratio and long confirmation latency in processing transactions. In this paper, we demonstrate the feasibility of tremendously increasing the success ratio of transactions and improving their execution efficiency by enhancing network nodes' connectivity and enforcing a balanced network channel capacity. To implement such ideas, multiple designs have been made. First, to extent nodes connectivity, we transform the nearly-linear ordered nodes into a star payment structured typology, and design an incentive financing mechanism to restructure a new landmark routing typology design. Especially, for the marginalized or dissociative nodes, we utilize specific financial loan strategies to encourage them to (re)join the system. Besides, we propose the Power Atomic Multi-Path Payments (Power AMP) traffic distribution method, which hierarchically allocates the bottleneck's currently-available capacity (to replace the random or equal division used in traditional AMP), and thus archives a balanced traffic usage. With such efforts, we improve the transaction success ratio and efficiency of transaction exertion by order of magnitude—compared with traditional PCN using the benchmark of landmark route, our method improves the success ratio by 11.06%.

Machine unlearning is the process through which a deployed machine learning model is enforced to forget about some of its training data items. It normally generates two machine learning models, the original model and the unlearned model, indicating training results before and after data items are deleted. However, recent studies find that machine unlearning is vulnerable to membership inference attacks—as the directivity of training and nontraining data (i.e., data items in the training set have high posterior probabilities), the attackers can utilize this property to infer whether an item has been used for original model training. Nevertheless, such attacks are incapable in label-only settings, in which the attackers are infeasible to get the posteriors. In this paper, we propose a new label-only membership inference attack scheme targeted at machine unlearning to eliminate the dependence on posteriors. Our heuristic is that injected turbulence on candidate samples will present different behaviors for training and nontraining data. Thus, in our scheme, the attacker iteratively query on the original/unlearned models and inject turbulence to change their predicting labels; it determines whether an item is having-been-delated by observing the disturbance amplitude. Extensive experiments (i.e., on MNIST, CIFAR10, CIFAR100, and STL10 data sets) show that our method achieves high inference accuracy (measured by AUC) in label-only settings, for example, AUC = 0.96 for MNIST data set. Besides, we analyze the existing countermeasures in mitigating inference attacks and find that our scheme can bypass most of them.

It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an all-round way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.

Wireless body area network (WBAN) consists of a number of sensors that are worn on patients to collect dynamic e-health records (EHRs) and mobile devices that aggregate EHRs. These EHRs are encrypted at mobile devices and then uploaded to the public cloud for storage and user access. To share encrypted EHRs with users effectively, help users retrieve EHRs accurately, and ensure EHRs confidentiality, a secure multi‐keyword search and access control (SMKS-AC) scheme is proposed, which implements encrypted EHRs access control under the ciphertext-policy attribute-based encryption (CP-ABE). SMKS-AC provides multi‐keyword search for accurate EHRs retrieval, supports the validation of decrypted EHRs, and traces and revokes the identity of users who leak private key. Security analysis shows that SMKS-AC is secure against chosen keyword and chosen plaintext attacks. Through theoretical analysis and experimental verification, the proposed SMKS-AC scheme requires less storage resources and computational costs on mobile devices than existing schemes.