As electric vehicle (EV) utilization in energy trading expands, protecting user identity privacy during transactions has become a critical area of research. Current privacy protection schemes face significant challenges, including the risks of identity leakage, insufficient protection against double signatures, and low verification efficiency. To address these challenges, this document proposes a lightweight identity privacy protection scheme based on blockchain technology from the consortium. The scheme introduces an improved linkable ring signature algorithm, which guarantees identity anonymity while effectively preventing double signature attacks. In addition, this paper develops a batch aggregation signature and verification algorithm designed to significantly improve verification efficiency in highly concurrent environments. Theoretical analysis and simulation experiments demonstrate that the proposed scheme outperforms existing solutions in security, computational efficiency, and communication overhead. Compared to existing solutions, this approach provides substantial improvements, making it a promising approach to secure user privacy within the rapidly evolving domain of EV energy trading.

Federated Learning is a widely used distributed machine learning framework that allows clients to collaboratively train a global model by uploading local gradients while keeping data stored locally, thus protecting user privacy. However, attackers can still infer local data from gradients. Recently, integrating differential privacy into FL has become a popular approach to ensure strong privacy guarantees. This paper proposes a Decentralized Federated Learning Framework with Enhanced Privacy and Optimized Fairness (DFL-EPOF). First, noise is added to local parameters before uploading, and a local differential privacy mechanism ensures data privacy. An adaptive privacy budget allocation strategy, based on data sensitivity, dynamically controls noise levels to balance privacy protection and model accuracy. Second, a weighted aggregation method based on clients' data volume, trustworthiness, and participation frequency is used to optimize fairness, ensuring balanced contributions. Finally, a decentralized blockchain-based architecture is implemented to enhance transparency and immutability, ensuring reliable model updates and data transmission. Experimental results show that DFL-EPOF improves privacy protection, fairness, and system robustness, balancing privacy and accuracy effectively.

Cross-chain interaction plays a crucial role in enhancing asset circulation and data sharing across diverse blockchain systems. Cross-chain identity authentication is the primary pre-requisite of cross-chain security interaction. However, existing cross-chain identity authentication protocol generally has issues such as insufficient decentralization, poor universality, and low authentication efficiency. These issues compromise the reliability of cross-chain interactions. Based on group signature and zero-knowledge proof, this paper proposes a cross-chain identity authentication protocol to address these concerns. The protocol employs Decentralized Identifiers(DIDs) as the global identity identifier of users, utilizes zero-knowledge proof to provide privacy for the verification of users' identities when joining the group, and then constructs the users' transaction signatures through group signatures to hide the users' identities. This approach not only reduces the risk of users' privacy leakage but also facilitates mutual recognition of identities among cross-chain systems. Finally, the security analysis and experimental analysis shows the correctness and efficiency of the proposed scheme.

With the rapid development of the Internet of Vehicles (IoV),Nevertheless, Federated Learning (FL) plays an increasingly crucial role in the secure data sharing within this field. Nevertheless, data in IoV settings frequently display Non-IID traits, which exerts a considerable influence on the efficiency and performance of FL. To enhance the model convergence speed and global performance under conditions of data imbalance and high heterogeneity, this paper presents a FL algorithm, named FedAE, that dynamically adjusts the local training epochs based on the changes in the AUC of model. FedAE assigns distinct local training epochs based on the quantity of training data in the initial stage and adaptively modifies them in subsequent stages in accordance with the dynamic changes in the AUC of both the global model and each client's model. Based on the performance disparity of the local training equipment, the algorithm establishes diverse training iterations to enhance the utilization of the local equipment and the training efficiency of the algorithm. Experimental results show that, compared to FedProx, the proposed approach significantly enhances model convergence efficiency and generalization ability. Specifically, FedAE reduces the loss by 26 %, demonstrating greater robustness, especially in scenarios with imbalanced data distributions.

As the core application of blockchain technology, smart contracts have been widely used in many fields such as finance, supply chain, and copyright management. Smart contracts are prone to various vulnerabilities that attackers can exploit to steal or freeze funds. Traditional vulnerability detection methods rely heavily on complex rules defined by experts, which are difficult to adapt to the explosion of smart contracts. Some recent studies of neural network-based vulnerability detection methods rely on contract source code, and the accuracy of bytecode-level vulnerability detection methods is low. To overcome the limitations of existing methods, we propose CV-IRTL, a new method for smart contract vulnerability detection. Specifically, CV-IRTL designs a vulnerability detection framework for smart contracts based on inverted residual network architecture and transfer learning. In particular, CV-IRTL enables vulnerability detection at the bytecode level, simplifies data preprocessing, utilizes transfer learning to better capture vulnerability characteristics and effectively address dataset imbalances. We have extensively tested CV-IRTL on a dataset containing six vulnerabilities. The experimental results show that the macro average F1-score is 90.75%, and the overall false positive rate is 9.6%, which is better than representative methods in performance.

The open architecture and inherent flexibility of the Android platform make it a prime target for malicious code penetration, posing huge security risks to users. Current graph-based detection methods have two limitations: high computational overhead and reliance on single-dimensional feature representation, which leads to poor detection performance. To address these challenges, this paper proposes a malicious code detection framework based on impact-sensitive subgraphs (LRS-GGCN), which integrates multi-level feature fusion and graph pruning techniques to improve efficiency and accuracy. First, the impact-sensitive subgraph is constructed using the LeaderRank algorithm and sensitive APIs to improve detection efficiency. Second, node-level features are enriched by fusing opcode structure and API semantic embedding to capture the syntactic and contextual properties of malicious code. At the edge level, API call frequency is combined to model interprocedural interactions and enhance the representation capability of the graph. Finally, a gated graph convolutional network (GGCN) synthesizes these heterogeneous features to achieve efficient and accurate malicious code detection. Experiments show that our method achieves an accuracy of 99.28%. In addition, the training time is reduced by about 90% compared to the unpruned baseline.

Secure neural network inference provides privacy guarantees for both the client and the server, and is an integral approach in Machine Learning as a Service Setting (MLaaS). However, the multilayer structure in the neural network introduces frequent activation function calculations, which causes large overhead. Most of the prior secure inference systems focused on designing cryptographic protocols to improve computational efficiency, but high computing and communication overhead are still bottlenecks in practicality. In this work, we refocus on the potential of shallow neural networks and propose a model with only one trainable layer to reduce the required computation. Our main contributions are in three-fold: 1) introduce training-free weights and formally prove their contribution in the model expressivity; 2) design the Self Enhanced Module that is more suitable for shallow models as an alternative for the activation function; and 3) propose a linear layer with multiscale and normalization property, named Nested & Norm Conv. We conduct extensive experiments on visual datasets and the results demonstrate the proposed single-layer trainable model holds promise as a viable platform for secure inference in practical applications.

HPC-cloud is becoming popular as it allows supercomputers to provide computing service with parallelism and high performance based on public cloud technology. Moreover, supercomputers across organizations are forming a network to scale the computational and storage capability of their service. However, the distributed computing process in the supercomputer internet requires a large amount of data transmission and access, arousing privacy and control problems. In this paper, we propose HPCBL, a blockchain-enabled decentralized data computing architecture that ensures private data sharing and computing in the Supercomputer Internet. We also propose a decentralized authentication scheme that entitles users the full control of their anonymous identity to support user private interactions with the Supercomputing Internet. The scheme supports anonymous self-derivative credentials for pair-wised access control and user-optional accountability without a trusted arbiter. We implemented a HPCBL prototype to empirically assess its performance.

With the development of the Industrial Internet of Things (IIoT), data sharing provides an important driving force for the innovative development of industry. By analysis and mining of massive data, enterprises can find new market opportunities and develop more competitive products and services. At the same time, frequent data breaches show that data faces serious security challenges in IIoT. Therefore, the security and privacy of data in IIoT must be ensured by means of identity authentication and access control technology. Traditional identity authentication methods usually only consider one-way authentication and have inherent security deficiencies that are insufficient to meet the needs of current IIoT systems. This paper proposes a blockchain-based mutual authentication scheme that replaces the traditional third-party intermediary with blockchain technology, to enhance the transparency and credibility of the identity authentication process. Moreover, the scheme combines attribute-based encryption with linkable ring signature to achieve both the protection of user identity privacy and identity tracking. Experimental results indicate that the proposed scheme demonstrates good scalability and usability.