Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability patterns undermines the effectiveness of a single unified model, and manual prompt engineering for massive weakness categories is unscalable.To address these challenges, we propose MulVul, a retrievalaugmented multi-agent framework designed for precise and broad-coverage vulnerability detection.MulVul adopts a coarse-to-fine strategy: a Router agent first predicts the top-k coarse categories and then forwards the input to specialized Detector agents, which identify the exact vulnerability types.Both agents are equipped with retrieval tools to actively source evidence from vulnerability knowledge bases to mitigate hallucinations.Crucially, to automate the generation of specialized prompts, we design Cross-Model Prompt Evolution, a prompt optimization mechanism where a generator LLM iteratively refines candidate prompts while a distinct executor LLM validates their effectiveness.This decoupling mitigates the self-correction bias inherent in single-model optimization.Evaluated on 130 CWE types, MulVul achieves 34.79% Macro-F1, outperforming the best baseline by 41.5%.Ablation studies validate cross-model prompt evolution, which boosts performance by 51.6% over manual prompts by effectively handling diverse vulnerability patterns.

To address the growing demand for privacy protection in machine learning, we propose an efficient and exact machine unlearning method for Large Models, called LMEraser. LMEraser takes a divide-and-conquer strategy with an adaptive prompt tuning mechanism to isolate data influence effectively. The training dataset is partitioned into public and private datasets. Public data are used to train the backbone of the model. Private data are clustered based on their diversity, and each cluster tunes a tailored prompt independently. This approach enables targeted unlearning by updating affected prompts, significantly reduces unlearning costs and maintains high model performance. Evaluations show that LMEraser reduces unlearning costs by 100 times compared to prior work without compromising model utility.

Data valuation quantifies the contribution of each training data, enabling harmful data detection and enhancing model robustness. However, existing methods are typically post-hoc and require fully trained models, making them computationally expensive and unable to detect harmful data early in training. We propose LiveVal, a real-time and trajectory-based data valuation method that assesses training data by analyzing their influence on the optimization trajectory. LiveVal includes three key innovations: 1) a real-time valuation framework with minimal overhead, seamlessly integrated into standard training processes; 2) an adaptive reference point mechanism that assesses data impact on generalization; and 3) a normalization technique that ensures fair comparisons across training stages. Theoretical analysis shows that LiveVal achieves directional alignment, boundedness, stability, and fairness. Experiments demonstrate that LiveVal achieves up to 180× speedup over baseline methods while maintaining robust performance across diverse models and datasets.

Recent advances in cryptocurrencies have sparked significant interest in blockchain technology. However, scalability issues remain a major challenge for wide adoption of blockchains. Sharding is a promising approach to scale blockchains, but existing sharding-based blockchains fail to achieve expected performance gains due to limitations in cross-shard transaction processing. In this paper, we propose X-shard, a blockchain system that optimizes cross-shard transaction processing, achieving high effective throughput and low processing latency. First, we allocate transactions to shards based on historical transaction patterns to minimize cross-shard transactions. Second, we take an optimistic strategy to process cross-shard transactions in parallel as sub-transactions within input shards, thereby accelerating transaction processing. Finally, we employ a cross-shard commit protocol with threshold signatures to reduce communication overhead. We implement and deploy X-shard on Amazon EC2 clusters. Experimental results validate our theoretical analysis and show that as the number of shards increases, X-shard achieves nearly linear scaling in effective throughput and decreases in transaction processing latency.

Although existing schemes improve blockchain throughput by allowing concurrent blocks to be appended to the blockchain, little attention has been devoted to adjusting blockchain throughput dynamically and deduplicating transactions between concurrent blocks. In this article, we propose AdaptChain, an adaptive scaling blockchain with transaction deduplication. When the transaction demand of users in the network is high, the blockchain expands to meet the demand; when the transaction demand is low, the blockchain shrinks to save communication and storage costs. Our transaction deduplication mechanism ensures that no duplicate transactions are added to the blockchain, thereby improving bandwidth utilization and achieving higher effective throughput. Besides, we randomly split the mining power of the system to achieve mining power load balancing and resist attacks. We formally analyze the blockchain security and implement the proposed prototype on Amazon EC2. Experimental results show that AdaptChain achieves dynamic and higher effective blockchain throughput.

Blockchain scalability is one of the most desired properties for permissionless blockchain. Many recent blockchain protocols have focused on increasing the transaction throughput. However, existing protocols cannot dynamically scale the throughput to meet transaction demand. In this paper, we propose Occam, a secure and adaptive scaling scheme. Occam adaptively changes the transaction throughput by expanding and shrinking according to the transaction demand in the network. We introduce a dynamic adjustment mechanism of mining difficulty and a mining power load balancing mechanism to resist various attacks. Furthermore, we implement Occam on Amazon EC2 cluster with 1000 full nodes. Experimental results show that Occam can greatly increase the throughput of the blockchain and the mining power utilization.

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve secure trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on an Ethereum blockchain and deployed on an Ethereum test network. In addition to implementing transactions between individual users, our scheme also allows transactions among multiple users. The experimental result proves that the cost of our scheme is acceptable.

Nowadays, multimedia content retrieval has become the major service requirement of the Internet and the traffic of these contents has dominated the IP traffic. To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, because in-network content caching can be directly utilized to respond users' requests, multimedia content retrieval is beyond content providers' control and makes it hard for them to implement access control and service accounting. In this paper, we propose an attribute-based accountable access control scheme for multimedia content distribution while making the best of in-network caching, in which content providers can be fully offline. In our scheme, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure the secure access control, which is also efficient in both space and time. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users' request process. Through the informal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.

With the dramatically increasing deployment of the Internet of Things (IoT), remote monitoring of health data to achieve intelligent healthcare has received great attention recently. However, due to the limited computing power and storage capacity of IoT devices, users' health data are generally stored in a centralized third party, such as the hospital database or cloud, and make users lose control of their health data, which can easily result in privacy leakage and single-point bottleneck. In this paper, we propose Healthchain, a large-scale health data privacy preserving scheme based on blockchain technology, where health data are encrypted to conduct fine-grained access control. Specifically, users can effectively revoke or add authorized doctors by leveraging user transactions for key management. Furthermore, by introducing Healthchain, both IoT data and doctor diagnosis cannot be deleted or tampered with so as to avoid medical disputes. Security analysis and experimental results show that the proposed Healthchain is applicable for smart healthcare system.

Nowadays, the Space Information Network (SIN) has been widely used in real life because of its advantages of communicating anywhere at any time. This feature is leading to a new trend that traditional wireless users are willing to roam to SIN to obtain a better service. However, the features of exposed links and higher signal latency in SIN make it difficult to design a secure and fast roaming authentication scheme for this new trend. Although some existing researches have been focused on designing secure authentication protocols for SIN or providing roaming authentication protocols for traditional wireless networks, these schemes cannot provide adequate requirements for the roaming communication in SIN and bring in critical issues, such as the privacy leakage or intolerable authentication delay. Observing these problems have not been well addressed, we design an anonymous and fast roaming authentication scheme for SIN. In our scheme, we utilize the group signature to provide the anonymity for roaming users, and assume that the satellites have limited computing capacity and make them have the defined authentication function to avoid the real-time involvement of the home network control center when authenticating the roaming users. The results of security and performance analysis show that the proposed scheme can provide the required security features, while providing a small authentication delay.

With an advancement of mobile communication technology, the space information network (SIN) has been proposed to meet the increasing demands of mobile communication due to its advantage of providing great expanding access services. In SIN, authentication is significant for the security to prevent the network resource from unauthorized access. However, the features of highly exposed links and extremely high propagation delay make it difficult to design a secure and fast authentication scheme for SIN. Although some existing researches have tried to design authentication protocols for SIN, they haven't taken the intolerable authentication delay and the risk of satellite compromising into consideration. Faced with these problems, we design a proxy signature-based authentication scheme for SIN, in which, the interaction process of authentication can be only implemented between the mobile user and the satellite node, thus reducing the long authentication implementation delay. Furthermore, we utilize the proxy signature to mitigate the risk of satellites being attacked. The results of security and performance analysis show that the proposed scheme can provide the required security and largely reduce the authentication latency.

We propose a compressive video sensing scheme based on user attention model (UAM) for real video sequences acquisition. In this work, for every group of consecutive video frames, we set the first frame as reference frame and build a UAM with visual rhythm analysis (VRA) to automatically determine region-of-interest (ROI) for non-reference frames. The determined ROI usually has significant movement and attracts more attention. Each frame of the video sequence is divided into non-overlapping blocks of 16 × 16 pixel size. Compressive video sampling is conducted in a block-by-block manner on each frame through a single operator and in a whole region manner on the ROIs through a different operator. Our video reconstruction algorithm involves alternating direction l1- norm minimization algorithm (ADM) for the frame difference of non-ROI blocks and minimum total-variance (TV) method for the ROIs. Experimental results showed that our method could significantly enhance the quality of reconstructed video and reduce the errors accumulated during the reconstruction.