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.

While sharding technology helps solve performance bottlenecks in traditional blockchain networks, it also introduces new challenges.Random node allocation may lead to uneven distribution of malicious nodes, causing performance differences and security risks.Existing reputation-based sharding schemes often overlook node performance characteristics and fail to address security concerns related to leader election.In addition, full sharding reorganisation drastically reduces the performance of the entire blockchain, both in terms of overhead and time.In this paper, we propose a reputationbased sharding dynamic reorganisation network sharding scheme, which integrates node performance and behavioural characteristics into the reputation computation, and at the same time adds alternative leaders to maintain the stability of the sharding system.Based on this, through the partial reorganisation algorithm of the sharding, the nodes are reasonably allocated to balance the computing power of each shard, effectively stimulating the liveness of the sharding system and improving overall safety and performance.Simulation results show that this sharding scheme effectively reduces the consensus failure probability, solves the collusion attack problem caused by the aggregation of malicious nodes within shards, and reduces the latency of the blockchain system.This provides strong support for the reliability and performance of the blockchain sharding system.

The mobile healthcare (mHealth) is a promising and fascinating paradigm, which can dramatically improve the quality of healthcare delivery by providing remote diagnosis and medical record sharing. Now, the mHealth faces serious challenges such as data leakage and unauthorised access currently. Attribute-based encryption (ABE) which has been employed for mHealth is an excellent cryptographic primitive of securing data sharing. However, there are still some security and efficiency issues in the ABE-based data sharing scheme for mHealth. Firstly, the explicit storage of access policy may expose the privacy of users. Secondly, the computation cost is high, especially in the mHealth with IoT devices. Thirdly, the authentication of access rights to shared data is usually performed by the centralised third parties or IoT devices with limited resources. To handle the above issues, this paper presents a privacy-preserving and efficient data sharing scheme. The scheme partially hides access policy to protect user's privacy, and introduces an offline mechanism in key generation and encryption phase to improve efficiency of mHealth. Furthermore, it also provides decentralised and trusted authentication of data access right based on blockchain. The security proofs and the experiment results demonstrate that the presented scheme has better security and efficiency.

The Internet of Things (IoT) connects everyday devices and generates real-time data that have greatly prompted business and life efficiency. The integration of IoT and blockchain has made IoT data management and storage more trustworthy. However, despite the immutability property contributes a lot to the trustable reputation of blockchain-based IoT systems, from a data processing perspective, it is desired to achieve skillful and secure blockchain rewriting for scenarios such as device data sharing. Existing blockchain rewriting solutions usually rely on centralized modifiers where the rewriting power is difficult to control or withdraw. In this article, we propose a new auditable redactable blockchain (RB) scheme named ACHR that supports self-management and mandatory revocation of the rewriting privilege. The scheme allows user devices to rewrite their blockchain transactions under strict auditing to ensure content security. To prevent centralization or rewriting power abuses, the revocation trapdoor can be computed compulsorily by an auditor when a redaction is published to the blockchain. We introduce a generic construction and an instantiation of the ACHR scheme for building the RB and prove its security. We provide a prototype implementation to demonstrate that our scheme is effective and efficient compared to the traditional blockchain-IoT system with immutability.

With the rapid development of cloud computing, big data, and mobile Internet technology, data sharing across different data centers is increasingly urgent. Meanwhile, data security risks such as unauthorized access and data abuse bring serious challenges to data security sharing. Due to its poor scalability, traditional access control technology cannot play its role in the distributed computing paradigms with massive data and dynamic network environment. Attribute-based access control (ABAC) model is a potential candidate for data sharing cross data centers. But it still faces some challenges, such as different attribute semantics among data centers, and slow query speed of attributes and policies. This paper proposes an architecture for access control across data centers by constructing a global attribute set (GAS) on the blockchain to manage attributes and policies. To achieve a high efficiency, the scheme utilizes world state rather than block for query of data attributes and policies, and employs trust degree to pre authorization. Security analysis and experimental results show that the model is secure and practicable.

Most existing virtual machine introspection (VMI) technologies analyze the status of a target virtual machine under the assumption that the operating system (OS) version and kernel structure information are known at the hypervisor level. In this paper, we propose a model of virtual machine (VM) security monitoring based on memory introspection. Using a hardware-based approach to acquire the physical memory of the host machine in real time, the security of the host machine and VM can be diagnosed. Furthermore, a novel approach for VM memory forensics based on the virtual machine control structure (VMCS) is put forward. By analyzing the memory of the host machine, the running VMs can be detected and their high-level semantic information can be reconstructed. Then, malicious activity in the VMs can be identified in a timely manner. Moreover, by mutually analyzing the memory content of the host machine and VMs, VM escape may be detected. Compared with previous memory introspection technologies, our solution can automatically reconstruct the comprehensive running state of a target VM without any prior knowledge and is strongly resistant to attacks with high reliability. We developed a prototype system called the VEDefender. Experimental results indicate that our system can handle the VMs of mainstream Linux and Windows OS versions with high efficiency and does not influence the performance of the host machine and VMs.

Online identities (OIDs) refer to accounts that Internet users create on Web platforms. In investigations of OID-involved criminal cases, investigators often encounter multiple OIDs used by the same criminal among a number of suspect OIDs. Recognizing such OIDs is referred to as OID linkage (OL) and is a critical task, because it helps investigators consolidate information from different sources, identify new investigation leads, perform further analysis by connecting seemingly unrelated cases, and finally ideate the real criminal. However, few existing OL techniques can achieve satisfactory performance in criminal investigation scenarios due to information asymmetry, information unreliability, and a lack of training data. We propose an unsupervised spectral co-clustering-based OL framework that takes OID access trajectories as linkage evidence. By leveraging a spectral co-clustering algorithm, we integrate access location consistency and access time consistency as heuristics to direct the OL process. Experiments performed using actual investigation data demonstrate the feasibility and promise of the proposed framework.

Memory forensics plays an important role in security and forensic investigations. Hence, numerous studies have investigated Windows memory forensics, and considerable progress has been made. In contrast, research on Linux memory forensics is relatively sparse, and the current knowledge does not meet the requirements of forensic investigators. Existing solutions are not especially sophisticated, and their complicated operation and limited treatment range are unsatisfactory. This paper describes an adaptive approach for Linux memory analysis that can automatically identify the kernel version and recovery symbol information from an image. In particular, given a memory image or a memory snapshot without any additional information, the proposed technique can automatically reconstruct the kernel code, identify the kernel version, recover symbol table files, and extract live system information. Experimental results indicate that our method runs satisfactorily across a wide range of operating system versions.

The increased use of social networking application on iPhone and iPad make these devices a goldmine for forensic investigators. Besides, QQ, Wechat, Sina Weibo and skype applications are very popular in China and didn’t draw attention to researchers. These social networking applications are used not only on computers, but also mobile phones and tablets. This paper focuses on conducting forensic analysis on these four social networking applications on iPhone and iPad devices. The tests consisted of installing the social networking applications on each device, conducting common user activities through each application and correlation analysis with other activities. Advices to the forensic investigators are also given. It could help the investigators to describe the crime behavior and reconstruct the crime venue.

This paper briefly introduces the challenges facing collection of volatile data in a target computer. Reasons to favor physical memory analysis are also given. After describing the related work of the memory analysis, details of a windows memory analysing method are given through which it is possible to extract useful information, such as running processes , current network connections, file contents, etc., from a memory image. The method is based on a data structure in Windows known as kernel processor control region, or KPCR. Besides, details of address translation from virtual address to physical address are thoroughly discussed and an algorithm of address translation for practice is given. This method is verified on Windows XP SP2, Windows 2003 Server SP2 and Windows Vista Home Basic.