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.

The sixth-generation (6 G) networks aim to achieve ubiquitous intelligent connectivity while ensuring extremely low latency, reducing energy consumption, and enhancing privacy protection. Mobile edge computing (MEC) offers an effective solution to reduce latency and energy consumption by leveraging resources near end devices for task offloading. However, MEC faces significant challenges in meeting the requirements of 6 G networks, including limited computational resources, high mobility, and strict data privacy demands. Efficiently allocating edge resources while preserving privacy has become a critical issue for realizing the objectives of 6 G networks. In this paper, we propose a privacy-preserving edge computing power network (EdgeCPN) model that jointly leverages the computing resources of edge computing nodes and protects sensitive computing power information through differential privacy methods. In addition, we propose a task matching problem that aims to minimize the privacy-budget-weighted energy consumption while ensuring privacy protection and meeting task requirements. We propose a dynamic graph-based multiagent reinforcement learning (MADRL) algorithm to find the optimal strategy for task matching and computing resource allocation with privacy protection. The results show that our proposed task matching model with energy and privacy tradeoffs can minimize the energy consumption in the matching process while ensuring privacy, and the algorithm can find the optimal strategy for task matching efficiently.

With the popularity of mobile devices, spatial-textual trajectory query has been deployed in applications such as trajectory-based navigation and travel route recommendation. Massive trajectory data have been outsourced to cloud servers for storage and sharing such as spatial keyword search. However, existing solutions only support similarity queries in the spatial dimension and still incur high storage and query costs, which cannot scale well in large-scale trajectory data scenarios. To solve the above issues, we first achieve an Efficient Range Query over Encrypted Trajectory Data (ERT) using Douglas-Peucker trajectory compression algorithm, random matrix multiplication, filtering-verification mechanism and polynomial fitting technology. Then, we further propose an enhanced Efficient Spatial Keyword Query over Encrypted Trajectory Data (ESKT) by constructing a unified spatial-textual index structure, which can find relevant trajectories that are within some arbitrary geometric range and contain all query keywords. Finally, we formally prove that our schemes are secure against chosen-plaintext-attack, and conduct extensive experiments to demonstrate that our schemes improve the query efficiency by almost 100× when compared with state-of-the-art solutions.

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.

To address the challenges posed by data silos and heterogeneity in distributed machine learning, privacy-preserving asynchronous Federated Learning (FL) has been extensively explored in academic and industrial fields. However, existing privacy-preserving asynchronous FL schemes still suffer from the problem of low model accuracy caused by inconsistency between delayed model updates and current model updates, and even cannot adapt well to Non-Independent and Identically Distributed (Non-IID) settings. To address these issues, we propose a Privacy-preserving Asynchronous Federated Learning based on the alternating direction multiplier method (PAFed), which is able to achieve high-accuracy models in Non-IID settings. Specifically, we utilize vector projection techniques to correct the inconsistency between delayed model updates and current model updates, thereby reducing the impact of delayed model updates on the aggregation of current model updates. Additionally, we employ an optimization method based on alternating direction multipliers to adapt the Non-IID settings to further enhance the global model accuracy. Finally, through extensive experiments, we demonstrate that our scheme improves the model accuracy by up to 12.53% when compared with current state-of-the-art solution FedADMM.

Federated learning not only realizes collaborative training of models, but also effectively maintains user privacy. However, with the widespread application of privacy-preserving federated learning, poisoning attacks threaten the model utility. Existing defense schemes suffer from a series of problems, including low accuracy, low robustness and reliance on strong assumptions, which limit the practicability of federated learning. To solve these problems, we propose a Robustness-enhanced privacy-preserving Federated learning with scaled dot-product attention (RFed) under dual-server model. Specifically, we design a highly robust defense mechanism that uses a dual-server model instead of traditional single-server model to significantly improve model accuracy and completely eliminate the reliance on strong assumptions. Formal security analysis proves that our scheme achieves convergence and provides privacy protection, and extensive experiments demonstrate that our scheme reduces high computational overhead while guaranteeing privacy preservation and model accuracy, and ensures that the failure rate of poisoning attacks is higher than 96%.

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.

Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility.

Exponential moments (EMs) are important radial orthogonal moments, which have good image description ability and have less information redundancy compared with other orthogonal moments. Therefore, it has been used in various fields of image processing in recent years. However, EMs can only take integer order, which limits their reconstruction and antinoising attack performances. The promotion of fractional-order exponential moments (FrEMs) effectively alleviates the numerical instability problem of EMs; however, the numerical integration errors generated by the traditional calculation methods of FrEMs still affect the accuracy of FrEMs. Therefore, the Gaussian numerical integration (GNI) is used in this paper to propose an accurate calculation method of FrEMs, which effectively alleviates the numerical integration error. Extensive experiments are carried out in this paper to prove that the GNI method can significantly improve the performance of FrEMs in many aspects.

Identifying the physical person behind an SNS account has become a critical issue in investigations of SNS-involved crime cases. It is a challenging task because information provided by users on an SNS platform could be false, conflicting, missing and deceptive. One way to gain an accurate profile of a user is to link up all their multiple accounts created on different social platforms, which is referred to as Account Linkage (AL). However, existing AL techniques suffer from the problem of information unreliability. Recent advances in location acquisition and wireless communication technologies give rise to new opportunities for AL. In this paper, we propose a framework that links up multiple accounts belonging to the same individual by comparing habit patterns extracted from user-generated location data. We built a topic model to capture users habit patterns in both spatial and temporal dimensions. Results of experiments carried out on a real-world dataset demonstrate the feasibility and validity of the proposed framework.