In vehicular ad-hoc networks (VANETs), real-time message exchange between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is essential for transportation efficiency and passenger safety. Ensuring message security and timeliness relies on an effective privacy-preserving authentication mechanism. A certificateless multi-receiver signcryption scheme enables a sender to transmit messages to multiple recipients simultaneously while avoiding key escrow issues. However, existing schemes require secure channels for key extraction, and cannot allow the Key Generation Center (KGC) to verify pseudonym legitimacy. To address these issues, this paper proposes an anonymous certificateless multi-receiver signcryption scheme for vehicular networks that supports signature aggregation and eliminates the need for secure channels. Security analysis demonstrates that the scheme achieves ciphertext unforgeability and confidentiality under the ECDL and CDH assumptions. Compared to existing schemes, it significantly reduces the computational cost of signcryption and unsigncryption.

Blockchain is facing scalability challenges. Rollups, as an off-chain scaling scheme, relieve the pressure on the main chain by transferring transactions to the layer 2 (L2) network. However, centralized sequencers remain vulnerable to single points of failure and weak censorship resistance. In this paper, we propose a decentralized rollup sequencer based on an optimized Tendermint consensus mechanism. This scheme effectively reduces the predictability of candidate node weights by integrating an enhanced Tendermint Weighted Round-Robin (WRR) algorithm with staking bucket splitting and randomized mapping strategies, while simultaneously incorporating a parallel transaction packing scheme to optimize the voting process. Furthermore, the proposed suspicious labeling mechanism combines on-chain challenge and fast rollback to improve transaction legitimacy verification. Experimental results show that the optimized system maintained 824 transactions per second (TPS) in a 30-node virtual machine environment, validating the scheme’s effectiveness in fairness and Sybil attack resistance. Additionally, the economic model and reward/punishment mechanisms enhanced decentralization, offering a new avenue for the extension of rollup technology.

Federated learning (FL) enables collaborative model training across distributed data silos without exposing raw data, with increasing deployment in critical domains such as healthcare consortiums and financial institutions where data sensitivity is paramount. However, existing FL systems rarely achieve both robust privacy protection and verifiable correctness in the presence of realistic adversaries. Current privacy mechanisms typically impose prohibitive overhead or become vulnerable under server–client collusion, while the verifiability of global updates is often absent. In this paper, we propose a practical verifiable aggregation scheme for privacy-preserving federated learning (PVA-FL) that delivers both confidentiality of local updates and end-to-end verifiability of aggregation processes. Secure aggregation is realized via homomorphic proxy re-encryption augmented with blinding, providing robust protection of local model updates against server-client collusion attacks. Furthermore, a lightweight verification framework based on aggregate signatures enables participants to independently validate that the released aggregation result genuinely reflects authenticated local contributions under the prescribed aggregation rule. Experimental evaluation demonstrates that PVA-FL achieves low computational and communication overhead while maintaining strong privacy and verifiability guarantees, establishing its practical viability for secure collaborative learning in distributed environments.

With the rapid development of Internet of Vehicles (IoV) and intelligent transportation systems, vehicles need to frequent communicate across heterogeneous trust domains such as service providers and operating platforms. Conventional authentication methods that depend on pre-shared keys or centralized institutions encounter two main obstacles: 1) low cross-domain trust transmission efficiency, which causes delays in authentication; 2) privacy leakage risk due to vehicle identity disclosure. To address these issues, this paper proposes an anonymous cross-domain authentication (ACA) technique based on SM2 group signature. Our ACA scheme integrates Chinese cryptographic algorithms and zero-knowledge proof technology to achieve efficient authentication while ensuring controllable anonymity of identities. Specifically, we design a group signature mechanism based on SM2 digital signature, supporting cross domain authentication of vehicles under anonymous conditions. Also, we introduce zk-SNARKs for lightweight verification, reducing the computational cost of roadside units (RSUs) by precomputing reference strings. Moreover, the combination of blockchain with traceable keys ensures that trusted institutions (TAs) can accurately trace the true identity when malicious behavior occurs. Security analysis shows that our ACA scheme satisfies traceability, conditional anonymity, and unforgeability under the random oracle model.

Smart contracts are immutable programs that automatically execute predefined logic. Once deployed, their underlying vulnerabilities are notoriously difficult to patch and highly susceptible to malicious exploitation, often leading to severe financial losses. Although existing vulnerability detection methods have demonstrated certain advantages, they still fail to achieve adequate structural–semantic coverage of vulnerability-relevant behaviors, as they are unable to jointly model opcode semantics, control-flow transitions, and data-dependency relations. To overcome these limitations, this paper proposes a novel smart contract vulnerability detection model named Cross-aligned Penetrative Graph Network (CPGNet). Specifically, CPGNet first constructs control flow graphs and data flow graphs from the abstract syntax tree, and combines them with opcode semantic embeddings to form a multidimensional initial code representation. Based on this representation, a cross-alignment mechanism is introduced to effectively capture and integrate the complex interactions between control-flow transitions and data-flow dependencies. Furthermore, an explicit–implicit feature penetration architecture is designed to inject shallow local opcode patterns into the deep semantic modeling process, enabling multi-source features to dynamically complement each other. By jointly modeling opcode semantics, control-flow structures, and data-dependency relations, CPGNet significantly enhances the representation capability for hidden and complex vulnerability patterns. Experimental results on two datasets show that CPGNet achieves stable performance, with F1-scores of 88.69% and 90.58% on the benchmark Ethereum dataset, and 78.10% and 71.53% on DIVE for reentrancy and timestamp dependency detection, respectively. These results verify the effectiveness of jointly modeling opcode semantics and graph-level structural dependencies.

In the domain of security, data protection ultimately hinges on secure storage of private keys. To this end, we propose a Provably Secure and fault-tolerant verifiable encryption scheme, in which private keys are encrypted and stored on the blockchain, allowing secure and decentralized key custody. It allows a user to manage only one key, reducing the complexity of key management. In addition, the proposed scheme features public verifiability, allowing anyone to verify whether the ciphertext corresponds to a valid private key without revealing the key itself. Furthermore, our scheme is fault-tolerant; even if the ciphertext stored on the blockchain undergoes some degree of tampering, it can still accurately retrieve the correct private key with probability 99

The Byzantine Fault Tolerance State Machine replication (BFT-SMR) protocol ensures the correct operation of distributed systems in the presence of faulty or malicious replicas. Traditional Byzantine protocols typically employ a single-leader model to drive consensus, whereas this model exhibits significant performance bottleneck: (1) The single-leader model requires all replicas to participate in the consensus, resulting in increased communication overhead among replicas and higher consensus latency; (2) The single-leader model incurs a communication cost of O(n^2) during the timeout phase. To address these issues, this paper proposes a BFT-SMR protocol, GP-BFT, based on multi-committee parallel consensus. GP-BFT initially divides online replicas randomly into multiple committees, forming a committee set, and follows a 2-chain commit mechanism to achieve parallel consensus among committees. By transitioning from full-replica consensus to intra-committee consensus, GP-BFT reduces commit latency while ensuring safety and liveness. Experimental results demonstrate that in scenarios with large-scale replicas and transactions, GP-BFT maintains high throughput with lower latency.

The rapid advancement of vehicular ad hoc networks (VANETs) has introduced significant security challenges, particularly in ensuring message authenticity and integrity over open wireless channels. Traditional cryptographic schemes based on number-theoretic assumptions are increasingly vulnerable to quantum attacks, motivating the integration of post-quantum cryptography. This article proposes a novel quantum-resistant lattice-based aggregate signature scheme (QR-LBAS) that provides quantum resistance and is tailored for VANETs. Based on the hardness of the ring short integer solution (Ring-SIS) problem over NTRU lattices, the proposed scheme enables efficient aggregation of multiple signatures into a compact form, thereby reducing communication overhead while maintaining strong security guarantees. In addition, the scheme supports efficient batch verification at roadside units (RSUs), improving scalability in high-density vehicular environments. Security analysis shows that the scheme achieves existential unforgeability under adaptive chosen-message attacks within the random oracle model. Comprehensive evaluation demonstrates improved computational and communication efficiency compared with existing post-quantum schemes. Furthermore, we provide a system-level performance analysis, showing that QR-LBAS achieves favorable scalability and reduced RSU workload, making it suitable for practical VANET deployments and long-term security in the quantum era.

Cryptographic wallets are fundamental for secure key management and asset protection in blockchain systems. While deterministic wallets, such as those formalized in BIP32, have become the de facto standard for hierarchical key derivation, their limited flexibility constrains practical usability. In particular, traditional deterministic wallets suffer from insufficient mnemonic recoverability and lack mechanisms for collaborative multi-party control, which undermines both resilience and user experience. To address these limitations, we introduce a novel framework for mnemonic-updatable hierarchical deterministic (HD) wallets. Our design leverages Chameleon hash functions as the core of the key derivation process, enabling controlled seed updates through trapdoor-based collision finding. Furthermore, we observe that chain codes obtained from hardened key derivation naturally function as threshold shares. Building on this, we propose a threshold-controlled subkey derivation scheme, allowing any subtree of the wallet to support secure, multiparty computation of child keys and recovery of parent chain codes. Both theoretical analysis and experimental results demonstrate that our scheme achieves low overhead and practical applicability in real-world scenarios.

As a fundamental method in the field of machine learning, federated learning is based on the principle of protecting the confidentiality of client data by requiring only the transfer of local models from clients to a central server, thereby avoiding the leakage of the underlying data. However, recent research suggests that even the exchange of model parameters between clients can indirectly reveal sensitive information about the local training data sets. Consequently, homomorphic encryption, which provides verifiable security without increasing communication overhead, has become one of the most commonly used methods. However, existing FL solutions using homomorphic encryption typically assume that all participating clients uniformly use public-private key pairs, which may inadvertently lead to the leakage of model gradients and thus compromise the confidentiality of client data. To address these issues, this paper proposes EMKPPA, an efficient multi-key privacy-preserving aggregation scheme specifically designed for FL, which aims to ensure secure and effective aggregation within a multi-key framework. The system adopts a dual-server architecture and exploits proxy re-encryption technology, combined with the additive properties of homomorphic encryption and the secure key exchange protocol of Diffie-Hellman, to achieve robust ciphertext transformation and aggregation processes, thereby ensuring the confidentiality of client data. Comprehensive security analysis and performance evaluation on various datasets provide strong evidence of the effectiveness and efficiency of EMKPPA in multi-key aggregation scenarios.

With the development of Distributed Vehicular Fog Services (VFS), the demand for vehicle authentication in high-speed mobility and cross-domain scenarios has grown significantly. However, traditional authentication schemes exhibit significant limitations in privacy protection, low latency, and multi-domain collaborative authentication, particularly in high-speed scenarios where vehicles frequently switch domains. Additionally, existing solutions relying on centralized authentication architectures are vulnerable to single points of failure, further exacerbating security risks.To address these challenges, this paper proposes a Blockchain-Assisted Traceable Cross-Domain Anonymous Authentication Mechanism (BTCAA), aimed at providing secure and efficient authentication for high-speed moving vehicles accessing VFS. BTCAA designs a flexible authentication process that allows vehicles to dynamically adjust authentication procedures based on their driving routes and introduces anonymity to protect user privacy. The mechanism adopts a lightweight design to reduce authentication overhead while supporting identity traceability, ensuring the ability to verify vehicle identities in dispute scenarios without compromising anonymity. The decentralized architecture eliminates the risk of single points of failure, enhancing system security. Security analysis confirms that BTCAA effectively ensures privacy protection, identity traceability, message integrity, and confidentiality. Performance evaluations further demonstrate its high practicality and efficiency while maintaining robust security and privacy protection.

Web 3.0, also known as the next-generation web, aims to create a smarter, more transparent, and user-autonomous Internet of Value through decentralized technologies and intelligent services. Decentralized applications (DApps), as foundational components of the Web 3.0 ecosystem, utilize blockchain and smart contracts to achieve decentralization, transparency, and resistance to tampering. However, DApps deployed on a single blockchain are unable to process invocation requests originating from nodes on other blockchains. Also, while a few DApps support cross-chain interactions, they often do not ensure atomic cross-chain invocations, which are crucial for maintaining consistency and reliability across different blockchain platforms. Moreover, cross-chain users urgently need to verify the trustworthiness of users on other chains, and those with multiple accounts hope that DApps can consolidate the data from these accounts. To address these challenges, this paper proposes TruAtom, a solution comprising the Atomic-Oracle chain and a smart community responsible for coordinating and processing cross-chain transactions. The Atomic-Oracle chain is a relay blockchain system that supports smart contracts. TruAtom enables atomic cross-chain invocations while leveraging the intelligent community on the Atomic-Oracle chain to compute user account reputations across multiple blockchains via Oracle mechanisms. Additionally, this paper proposes an incentive mechanism to enhance the motivation and credibility of member in smart community. Furthermore, this paper validates the security of this scheme through security analysis. Experimental results indicate that TruAtom achieves lower latency and higher success rates for cross-chain invocations compared to AtomCI.

In Vehicular Ad-hoc Networks (VANETs), vehicles exchange information in real-time by collaborating with Roadside Units (RSUs) and On-Board Units (OBUs) to enhance traffic efficiency and safety. Therefore, guaranteeing the integrity and confidentiality of messages, as well as the authenticity of the sender's identity, is crucial for establishing reliable communication. To address the aforementioned issues, this paper proposes an improved unforgeable aggregate signcryption scheme for VANETs, where a receiver can efficiently verify the legitimacy of multiple message sources simultaneously. The scheme enhances the security and privacy of VANETs by deploying a mapping between anonymous identities and public keys on a blockchain. Security analysis demonstrated that, under the hardness assumptions of the Elliptic Curve Discrete Logarithm Problem (ECDLP) and the Computational Diffie-Hellman Problem (CDHP), this scheme can achieve ciphertext unforgeability and confidentiality. Compared to existing related schemes, our proposed solution significantly reduces computational costs in both the signcryption and unsigncryption (especially in batch processing) phases.

The widespread application of Industrial Internet of Things (IIoT) has profoundly transformed modern industrial production methods. However, the large scale of industrial data, the diversity of data sources, and the heterogeneity of data structures make data maintenance exceptionally challenging. The editability techniques have been introduced into blockchain to leverage its distributed and editable features for data maintenance and management. However, existing solutions mainly focus on achieving editability while overlooking data management personnel's privacy protection and lacking effective modification verification mechanisms to ensure data reliability. To address these issues, this paper proposes a verifiable decentralized data modification mechanism supporting accountability (DDMA) for securing IIoT. Our DDMA mechanism not only achieves the redactability of blockchain but also protects the privacy of data management personnel and provides reliable clues for subsequent accountability. Additionally, the DDMA mechanism offers an efficient method for modification verification, enabling rapid validation of whether on-chain data has been modified. Theoretical analysis demonstrates that DDMA meets the requirements of modification authorization, behavior traceability, and efficient modification verification. Experimental results indicate that DDMA can reduce modification and verification overheads.

In an open and complex communication environment, VANET is vulnerable to malicious attacks, and traditional data sharing methods are inadequate for supporting the massive data interactions in VANET. Existing data sharing schemes for VANET struggle to achieve high efficiency and credibility, along with traceability of user identity, while ensuring the privacy of data and user identity. Addressing these challenges, this paper proposes a UAV-assisted VANET data sharing (UVDS) system based on consortium blockchain. To address the issues of certificate management and key escrow in data access control, this scheme introduces a certificateless proxy re-encryption mechanism. This mechanism can not only protect user identity privacy, but also trace malicious users’ real identities when necessary. Meanwhile, it can prevent unauthorized access by collusion between proxies and data requesters. To enhance authentication efficiency, aggregation technology is used for batch authentication of user identity. For large-scale traffic data storage, the combination of blockchain and IPFS distributed storage technology not only effectively alleviates the pressure of massive traffic data storage, but also assists data applicants in verifying the correctness of re-encrypted data. Security analysis shows that the UVDS system guarantees the secure sharing of traffic data. Theoretical analysis and experimental results demonstrate that this system has significant advantages in computational efficiency and is practical in VANET application.

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.

To address the challenges of high cross-shard transaction ratios and delays in cross-shard transaction verification encountered in optimizing blockchain performance through sharding technology, this paper proposes a blockchain layered sharding algorithm. Based on the long-term accumulated transaction data in the blockchain, this method predicts and optimizes the shard distribution of transactions by analyzing the transaction characteristics between accounts, thereby reducing the number of transactions that need to be conducted between different shards. A blockchain transaction sharding algorithm (OSABTC) was designed to optimize conventional shards (i-shard) and bridge shards (b-shard) in layered sharding through transaction characteristics. By reducing the cross-shard transaction ratio and increasing the proportion of bridge shard transactions, the problem of high cross-shard transaction ratios in transaction sharding was effectively resolved. Experimental comparisons show that, compared with the Random Sharding Algorithm (RSA), the OSABTC algorithm can reduced the cross-shard transaction ratio and increased the bridge shard transaction ratio, while transaction latency was reduced. These results demonstrate the effectiveness of the OSABTC algorithm in significantly enhancing the performance and efficiency of blockchain systems.

With the increasing popularity of the Internet of Things (IoT), network security faces significant challenges due to emerging network attacks. Therefore, ensuring user security and privacy has become a crucial research focus in Vehicular Ad Hoc Networks (VANETs). Over the past decade, numerous signature schemes have been proposed for VANETs, particularly certificateless signatures which have made notable progress in addressing issues related to certificate management and key escrow. However, traditional signature schemes based on conventional security assumptions encounter new security challenges with the advent of quantum computing. In this regard, lattice-based cryptography is considered promising in resistance quantum attacks. Consequently, we propose an efficient NTRU lattice-based certificateless signature scheme for VANETs (NTRU-CLS) that is provably unforgeability in the random oracle model based on the Small Integer Solution (SIS) assumption. The security analysis demonstrates that our scheme provides unforgeability both Type-I and Type-II adversaries in the random oracle model by utilizing the SIS problem on the NTRU lattice. Furthermore, comparative results indicate that our proposed scheme offers significant advantages in terms of computation cost compared to existing approaches while also minimizing communication overhead when compared to other lattice-based schemes. Overall, our scheme has better meet the requirements of VANET environments in terms of security, communication overheads, and computation costs.

The interoperability of blockchain cross-chain systems still poses a risk of data and user identity leakage. Concurrently, regulators encounter challenges in effectively verifying user identities, and the discrepancy between cross-chain regulation and the protection of user identity and privacy remains unaddressed. To address the aforementioned issues, this paper proposes an Identity-Verified Multi-Notary Cross-Chain Traceability and Regulation (IMCTR) system. The IMCTR system achieves cross-chain activity supervision through a supervision chain, mitigates centralization risks by utilizing a notary chain, and designs a multi-receiver encryption scheme to enhance the efficiency of signcryption and de-signcryption in cross-chain interactions while reducing computational overhead for users and safeguarding identity privacy. Additionally, regulators can effectively verify the receiver’s identity, swiftly identify unauthorized recipients, and prevent information leakage. Finally, a consortium blockchain cross-chain system that facilitates collaboration among multiple business chains has been established. The theoretical analysis and experimental results demonstrate that IMCTR significantly reduces the computational costs associated with encryption/decryption and signcryption/de-signcryption processes when compared to existing multi-receiver encryption schemes. Furthermore, IMCTR exhibits improved efficiency in cross-chain operations, as well as in identifying and tracking illicit users. This validates the effectiveness and high efficiency of IMCTR.

The Industrial Internet of Things (IIoT) is increasingly utilized in the industrial sector to enhance production efficiency, improve product quality, and reduce costs. Due to its decentralized nature, tamper-resistance, and traceability, consortium blockchain has extensive applications in IIoT. Byzantine Fault Tolerance (BFT) consensus protocols are widely used in consortium blockchain systems. However, typical BFT consensus protocols cannot detect and remove faulty nodes, thereby introducing security and performance issues, which are particularly severe for IIoT due to its weak security and limited resources. To address these issues, we present Fault-Removable Byzantine Fault Tolerance (FRBFT) protocol, a synchronous BFT consensus protocol that can detect and remove faulty nodes. If a faulty node keeps silent or equivocates, it could be detected and removed by all the honest nodes from the system in the next iteration. Our evaluations show that FRBFT can detect and remove 8 faulty nodes out of 17 nodes within 1.4 s, with a throughput of 19.8 Kops/sec and a latency of 118.7 ms, which has better security and performance than Sync Hotstuff and is more suitable for application in IIoT scenarios.