M. Lavanya, V Thiruppathy Kesavan, G. Sathya, R. Gopi
Electric Vehicles (EVs) that use Internet of Things (IoT) networks often involve the exchange of sensitive data between vehicles, charging stations, and other infrastructure, making data security and user privacy critical concerns. Existing methods for securing data in EV IoT networks rely on centralized systems, which create a single point of failure and are vulnerable to cyberattacks, data breaches, and unauthorized access. Furthermore, these systems struggle to address privacy concerns effectively, especially regarding user location and personal information. The proposed solution introduces a Blockchain Technology-based privacy preservation framework for EV networks (BCT-PP-EV). This framework leverages blockchain's decentralized nature to provide secure, transparent, and tamper-proof data exchanges. It ensures user privacy using cryptographic techniques such as zero-knowledge proofs (ZKP) and data anonymization, allowing privacy-preserving transactions without compromising data accuracy. Blockchain's immutability guarantees the integrity of the shared data, while smart contracts automate secure and efficient interactions within the network. The proposed method enhances secure data sharing while preserving privacy across EV IoT networks. By decentralizing data storage and enabling transparent auditing, BCT-PP-EV fosters trust among stakeholders and reduces the risks of unauthorized access or data manipulation. Preliminary findings suggest that implementing BCT-PP-EV significantly improves the security and privacy of data exchanges in EV networks, providing a scalable and resilient solution for the evolving smart transportation ecosystem. Experimental results demonstrate that BCT-PP-EV achieves 94.91% secure data sharing efficiency, reduces data breaches by 92.84%, and ensures data accuracy of 91.44%. Additionally, the framework exhibits high scalability of 96.57% with increasing network nodes, while maintaining controlled latency and throughput. Although unauthorized access resistance is measured at 24.71%, indicating scope for further improvement, the overall results confirm that BCT-PP-EV provides a robust, scalable, and privacy-preserving solution for next-generation smart transportation systems.
The increasing reliance on centralized ride-sharing structures, and exposes users to risks such as system failures and privacy breaches. manipulation, single points of failure, and privacy violations. In addition, high commission fees imposed by such platforms reduce the net earnings of drivers and compromise fairness within the ecosystem. To address these inefficiencies, this project introduces a decentralized vehicle borrowing system and carpooling, based on Ethereum Compatible blockchain and smart contracts. The proposed platform eliminates intermediaries by allowing KYC-verified drivers, passengers, and vehicle owners to interact directly, thereby building trust and operational transparency. All ride postings, bookings, car borrowing transactions, and agreement verifications are recorded immutably through smart contracts. Identity proofs, vehicle documents are cryptographically signed through MetaMask and uploaded via a decentralized file system (IPFS), ensuring authenticity and wallet-to-user binding. For drivers who borrow cars, temporary verification is enabled after signing a smart-contract-based agreement linked to the vehicle's verified owner. To maintain decentralization without depending on an administrator, the system introduces a Global Dispute Center where only users who fulfill certain predefined conditions—having verified their identity—can participate in resolving concerns through a voting process. This decentralized decision-making process enhances fairness and trust. Additionally, a structured post-ride rating system builds mutual accountability and trust among participants, while integrated CO₂ tracking encourages environmentally conscious behavior. Together, these features help minimize traffic load, support conscious travel habits, and build a reliable, user-governed mobility system that is secure, transparent, and environmentally supportive—functioning entirely without any centralized authority or administrative oversight, thereby ensuring long-term sustainability. To address these limitations, this project proposes a blockchain-powered peer-to-peer carpooling and vehicle borrowing system that enables direct interaction between passengers, drivers, and vehicle owners without intermediaries. The platform utilizes smart contracts to automate agreements, MetaMask for secure authentication, and IPFS for decentralized storage of essential records. By shifting operational control to users, the system enhances transparency, fairness, and reliability in transactions. Conventional mobility services also face issues such as opaque processes, inefficient dispute handling, and limited mechanisms for conflict resolution. Drivers often lose a substantial portion of their income to service fees, while users lack trust in centralized decision-making systems. Furthermore, minimal emphasis is placed on promoting environmentally responsible travel practices. Motivated by the need for an open and community-driven mobility platform, this research aims to establish a distributed ecosystem that eliminates third-party dominance and ensures tamper-proof record keeping. The system incorporates KYC-based digital identity verification, smart contract-enforced agreements, decentralized dispute resolution through voting, and CO₂ emission tracking to encourage sustainable transportation. The scope of the project includes enabling secure ride booking, vehicle borrowing under verified ownership, and democratic dispute resolution among verified users. By leveraging distributed networks and digital wallets, the platform presents a scalable and sustainable alternative to centralized ride-sharing models.
Collins Izuchukwu Okafor, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae Min Lee
Vehicular-to-Everything (V2X) communications promise unprecedented safety and efficiency gains but remain vulnerable to confidentiality breaches such as eavesdropping, traffic analysis, and man-in-the-middle attacks. We propose ConfidSPEC-V2X, a focused hybrid framework that integrates continuous-variable quantum key distribution (CV-QKD), a multi-agent deep reinforcement learning (DRL), and an Ethereum-based permissioned blockchainPureChainpublic-key infrastructure (PKI) to deliver information-theoretic secrecy, dynamic traffic obfuscation, and tamper-proof key management. In the quantum module, CV-QKD transceivers embedded in On-Board Units (OBUs) and Roadside Units (RSUs) establish symmetric keys resilient to passive interception and capable of immediate eavesdropping detection. The Artificial Intelligence (AI) module employs multi-agent DRL agents at RSUs to learn optimal dummy-traffic injection policies that obfuscate real V2X message patterns against statistical inference. The blockchain module leverages PureChain smart contracts to register, rotate, and timestamp vehicle public keys, ensuring that any man-in-the-middle attempt to forge or replay keys is invalidated. We implement and evaluate ConfidSPEC-V2X within an OMNeT++/Veins simulation under realistic urban mobility scenarios, measuring the quantum bit error rate, key generation throughput, obfuscation entropy, and key management latency. Results demonstrate that our framework achieves robust confidentiality protection with minimal performance overhead.
Background: Airport security demands sub-second, high-throughput identity verification while increasingly stringent privacy regulation prohibits the centralized accumulation of passenger data. Existing deployments copy complete passenger profiles to every checkpoint terminal, multiplying the data breach surface at each journey touchpoint and conflicting with GDPR data minimization requirements. Methods: This paper presents BIPV (Blockchain-based Identity and Privacy Verification), a system that resolves this tension through programmable zero-knowledge proofs. BIPV anchors only cryptographic references on a Hyperledger Fabric consortium blockchain; passengers prove eligibility at checkpoints via Circom-compiled Groth16 zk-SNARKs that confirm policy compliance without disclosing any underlying personal attributes. We detail the Circom circuit design for airport policy predicates (AgeVerifier, NationalityChecker, DocumentValidator), a proof pre-computation and caching strategy that eliminates gate-lane latency, and a Hyperledger Fabric consortium governance model that anchors verification keys without recording passenger movement. Results: Our prototype achieves 0.42 s mean verification latency, 2,380 passengers per checkpoint per hour, and a 94.7% reduction in PII exposure relative to centralized baselines, evaluated across 1,000 simulated verification sessions. Security analysis confirms resistance to credential forgery, replay attacks, and consortium collusion under standard cryptographic assumptions. Conclusions: BIPV satisfies GDPR data minimization requirements, ICAO Annex 17, and IATA One ID guidelines. Beyond aviation, the BIPV model generalizes to any domain requiring high-assurance, high-throughput identity verification under privacy obligations.
Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load.
Vehicle trajectory prediction in Internet-of-Vehicles requires collaborative learning over sensitive trajectories under intermittent connectivity and partially trusted participants. ChainDrive-FL-VRA coordinates semi-asynchronous federated learning on a permissioned consortium ledger using Practical Byzantine Fault Tolerance (PBFT), while keeping raw trajectories and raw model-update tensors off-chain. Each client submits an on-chain header containing a commitment and hash of the local update, together with zero-knowledge proofs that certify [Formula: see text]clipping and anchor-consistency. Validators admit only proof-checked updates, compute staleness- and reputation-aware robust weights, and publish a proof of correct aggregation that binds the aggregation commitment and the committed global model hash to the admitted committed updates under fixed-point weights. A contextual-bandit trigger selects aggregation timing under client churn. Experiments on NGSIM US-101 and I-80 show improved ADE/FDE/RMSE and improved robustness under staleness and anomalous updates, while on-chain artifacts remain at kilobyte scale per update and per aggregation event.
Shuchi Priya, Sushil Kumar, Anjani Anjani, Ahmad M. Khasawneh · 5 authors
Smart vehicles integrated with the Internet of Things (IoT) provide rich data for traffic management, safety, and liability services; however, existing blockchain-enabled vehicular architectures still struggle with consensus scalability, heavy centralized validation, limited interaction-based corroboration, incomplete attack coverage, and rapid ledger growth. In particular, many schemes either optimize single-layer consensus or embed detailed reputation information into every transaction, while pushing most validation to central servers. This leads to bottlenecks under dense traffic and leaves replay, Sybil-assisted 51% attacks on roadside units (RSUs), and man-in-the-middle tampering only partially addressed. In this context, this paper proposes a novel hierarchical blockchain for vehicular IoT (HBV-IoT) model to address the above challenges. An independent transaction for periodic vehicle status reporting and an interaction-based transaction for corroborating data between vehicles in proximity are presented. Three smart contracts are designed to automate the validation and processing of transactions, and to identify compromised or malicious vehicles within the HBV-IoT network. Algorithms for distributed consensus to accept transactions into the blockchain and for vehicle reputation management to enforce edge-level filtering and down-weighting of malicious nodes are implemented. Simulation results demonstrate significant improvements compared to conventional vehicular blockchain approaches, with performance gains validated by 95% confidence intervals. The model supports practical applications, including real-time traffic monitoring, automated e-challan issuance, intelligent insurance claim processing, and blockchain-based vehicle registration.
Abstract The integration of connected vehicles into 5G networks introduces stringent requirements in terms of latency, reliability, security, and adaptability that are not fully addressed by existing 5G architectures. In particular, Vehicle-to-Network (V2N) services must operate under high mobility, dynamic traffic conditions, and multi-tenant environments, while remaining resilient to security threats and operational anomalies. In this paper, we propose a 5G-based architecture for connected vehicles that addresses these challenges by combining deterministic communication, secure resource coordination, and runtime monitoring mechanisms. To enhance communication predictability beyond best-effort transport, the architecture integrates Time-Sensitive Networking (TSN) within the 5G transport network. Secure and transparent coordination across multiple stakeholders is supported through Distributed Ledger Technology (DLT), mitigating risks associated with centralized control. The architecture further incorporates heterogeneous data collection to enable adaptive resource management, as well as Runtime Verification and an AI-based anomaly detection system to monitor system behavior and network traffic in real time. By jointly addressing determinism, security, and adaptability within a unified 5G architecture, this work contributes a comprehensive foundation for reliable and secure connected vehicle services.
Location privacy insparseInternet of Vehicles is difficult to ensure due to limited anonymity, predictable mobility, and prolonged tracking windows. Existing silent-period and pseudonym-based schemes generally assume dense traffic and thus degrade under low-density conditions. This work proposes ZK-V2XChain, a lightweight privacy-preserving framework that integrates a Random Silent Period (RSP) mechanism with blockchain-based Identity Token (IT) authentication and Zero-Knowledge Proof (ZKP) validation. The framework explicitly models sparse-network behavior and enables adaptive, verifiable privacy without compromising efficiency. We design a privacy-preserving IT issuance process using simulated smart contracts and implement decentralized IT verification through RSU–blockchain interaction. Using SUMO mobility traces, ns-3.45 simulations, and MATLAB-based privacy analytics, results show that ZK-V2XChain achieves higher entropy than SAP, RFPM, GLS, and CPS, and approaches the performance of OBS. The maximum anonymity-set size reaches 10.88, and communication overhead remains low (940 bytes). Ablation studies highlight the complementary roles of RSP, ZKP, and blockchain in balancing uncertainty, responsiveness, and issuance stability.
The current methods don't meet the security and performance needs of Internet of Vehicles (IoV) apps, and they also don't give the end user a low-latency, secure edge-computing service at the same time, while in the context of vehicles. This study presents a blockchain-enabled edge computing architecture that employs Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) for consensus, aiming to simultaneously enhance latency, energy efficiency, and security. The containerised architecture uses Hyperledger Fabric with Kubernetes to efficiently manage micro-services and move tasks off of them. In urban, suburban, and highway settings, the framework consistently outperforms baseline algorithms, with a 30–45% improvement in end-to-end latency and a 55% reduction in energy use under moderate to heavy loads. The system finished more than 95% of its tasks while keeping block consensus times under 1.2 seconds at peak loads. The architecture also showed consistent performance with different levels of vehicle density and used zero-knowledge proofs with attribute-based security to protect data from cyber threats from bad actors. These findings indicate that the integration of DDQN and blockchain will mitigate security issues in the Internet of Vehicles (IoV) by enabling secure edge computing for future vehicular networks.
In this paper, a novel security framework for industrial internet of things (IIoT) and vehicular networks is proposed, integrating blockchain technology with advanced encryption and data classification mechanisms to enhance data integrity, confidentiality and trustworthiness. The work employed ChaCha20-Poly1305 encryption to safeguard the data transaction to local cluster nodes. A private blockchain gateway then processes the encrypted data, classifying it based on confidentiality levels, and directing storage either to cloud servers or the interplanetary file system (IPFS). To ensure data integrity, a proof of authority consensus mechanism within the blockchain is incorporated, while zero knowledge proof (ZKP) methods are used for authentication and secure data access. Empirical evaluations demonstrate that our framework achieves a data transmission security rate of 97.5%, with an average encryption and decryption latency of 150 milliseconds, significantly improving over traditional methods. The proof of authority consensus mechanism exhibits a transaction validation speed of 300 transactions per second, showcasing enhanced efficiency compared to standard blockchain models. Furthermore, the integration of ZKP challenges results in a 30% reduction in unauthorised access attempts, indicating a substantial improvement in overall security. This work emphasises the need for continuous innovation in addressing the various security issues in IoT, ultimately advancing the operational efficiency and security of these systems.
Munawar Hasan, Apostol Vassilev, Edward Griffor, Thoshitha Gamage
The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk-SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that a statement about its own internal state is true, without revealing sensitive or proprietary data about that state. This paper proposes Hermes Seal: a zk-SNARK-based ZKP framework for enabling privacy-preserving, verifiable communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. The framework allows autonomous systems to generate cryptographic proofs of perception and decision-related computations without revealing proprietary models, sensor data, or internal system states, thereby supporting interoperability across heterogeneous autonomous systems. We present two real-world case studies implemented and empirically evaluated within our framework, demonstrating a step toward verifiable autonomous system information exchanges. The first demonstrates real-time proof generation and verification, achieving 8 ms proof generation and 1 ms verification on a GPU, while the second evaluates the performance of an autonomous vehicle perception stack, enabling proof of computation without exposing proprietary or confidential data. Furthermore, the framework can be integrated into AV perception stacks to facilitate verifiable interoperability and privacy-preserving cooperative perception. The demonstration code for this project is open source, available on Github.
Carpooling System (CS) is traveling in a carpool where many people travel in one car. It provides users with a number of vehicles spread across the city. Sensitive information, such as the Ride Sharing (RS) user’s identity, zone, access code, etc., is transmitted through public CS. Due to data breaches, it is necessary to maintain data confidentiality and adequately secure ridesharing users’ data. Criminals use this information to steal information from the CS. Therefore, since attackers can access RS users’ details for illegitimate purposes, developing secure authentication protocols is critical. Traditional car-sharing systems also follow a centralized structure for data sharing. Yet, car-sharing systems also have security challenges. To address this problem, this paper suggests using the Blockchain-based Decentralized Secret Ride Communication System (BDSRCS) for secure data transmission in CS. First, the Proxy Attribute-based Encryption (PAE) approach is utilized to encrypt the RS user’s details in an unreadable format. Subsequently, the BDSRCS approach is used for secure chain link-based communication in the network. This method prevents unauthorized access to data and decentralized data sharing in the network. Thereafter, the Delegated Proof of Stake Key Validation (DPSKV) technique is used to check the primary keys for peer-end verification to hand over the encrypted data. Numerical outcomes establish that the proposed BDSRCS-PAE model enhances the detection rate of suspicious manners and increases the security performance compared with other approaches.
In recent years, the distributed ledger systems (DLS) has become an indispensable approach for creating anonymous payment systems in Smart City infrastructures (in transportation and industrial systems, energy planting and distribution, etc.). Although users in such systems are identified indirectly, but through cryptographic protocols, there is a class of attacks aimed at deanonymizing participants by analyzing transactions and constructing a graph of relationships between addresses. To implement these attacks, intruders use statistical analysis methods, graph theory, and specialized utilities for comparing data from external sources, such as exchanges. An analysis of related works in this domain has shown that compromising cryptographic primitives is not a prerequisite for deanonymizing DLS users; in some cases, analyzing public ledger data and the behavioral characteristics of DLS participants is sufficient. The goal of this research is to preserve privacy and develop protocols that minimize the risks of deanonymizing participants in Smart City ledgers built on the UTXO model. This paper presents a developed framework consisting transaction generator, an analyzer for modeling deanonymization attacks, and protocols designed to protect against such attacks. The experimental study has shown that the use of the CoinJoin protocol significantly complicates the deanonymization task and leads to a decrease in the deanonymization accuracy.
The Fifth Industrial Revolution (Industry 5.0) foregrounds human–machine collaboration, sustainability, and resilience as organizing principles for next-generation cyber-physical systems. Yet the identity and access management (IAM) architectures inherited from Industry 4.0 remain perimeter-centric, policy-static, and blind to the behavioral dynamics of human–AI teaming. This paper introduces the Human-Centric Zero Trust Identity Architecture (HC-ZTIA), a novel framework that repositions identity as the adaptive control plane for Industry 5.0 environments. HC-ZTIA integrates three mutually reinforcing innovations: (1) a Joint Embedding Predictive Architecture (JEPA)-driven Behavioral Identity Assurance Engine (BIAE) that learns abstract world models of operator and machine-agent behavior to perform continuous, context-aware identity verification without relying on raw biometric surveillance; (2) a Privacy-Preserving Adaptive Authorization Protocol (PP-AAP) employing zero-knowledge proofs and federated policy evaluation to enforce least-privilege access across human, non-human, and hybrid identity classes while satisfying data-minimization mandates; and (3) a Resilience-Oriented Trust Degradation Model (RO-TDM) that guarantees fail-safe identity governance under adversarial, degraded, or disconnected operating conditions characteristic of operational technology (OT) and critical infrastructure. The framework is grounded in the Agile-Infused Design Science Research Methodology (A-DSRM) and formally extends NIST SP 800-207 and the CISA Zero Trust Maturity Model by addressing five identified gaps in human-centric identity governance. We present the formal system model, threat model, architectural specification, and a multi-scenario evaluation spanning energy-sector OT, smart manufacturing, and vehicle-to-everything (V2X) environments. Simulation results, validated through Monte Carlo trials with 95% confidence intervals, demonstrate that HC-ZTIA reduces identity-related breach exposure by 73.2% (±4.1%) while maintaining sub-200 ms authorization latency, offering a principled bridge between Zero Trust rigor and Industry 5.0 human-centricity.
The integration of Blockchain and Reconfigurable Intelligent Surfaces (RIS) offers a secure, transparent, and high-performance foundation for 6G-enabled V2X communication. This chapter explores how blockchain's decentralized trust, immutable records, and smart contracts complement RIS-enabled wireless optimization to enhance signal reliability, reduce latency, and prevent tampering in dynamic vehicular environments. The proposed framework strengthens authentication, data integrity, and trust management while supporting advanced applications such as secure platooning and decentralized vehicular data marketplaces. Key challenges related to scalability, energy efficiency, and interoperability are examined, along with future prospects involving AI-driven optimization, post-quantum security, and autonomous RIS-blockchain coordination. Overall, the chapter demonstrates how merging programmable wireless control with distributed ledgers enables resilient and trustworthy next-generation mobility.
This study proposes a mobility control architecture that replaces conventional physical distance-based autonomous driving with a network-based logical spatiotemporal occupancy reservation system. Instead of relying solely on onboard sensors, vehicles reserve future movement regions as packetized occupancy rights across a communication infrastructure. A multi-layer consensus mechanism—combining efficiency, safety, and legal validation algorithms—determines vehicle behavior, while smart contracts and distributed ledgers record risk evaluation and liability allocation in real time. This approach enables high-density traffic flow beyond traditional braking constraints, improves road utilization efficiency, and enhances legal accountability in autonomous mobility networks. The framework also integrates infrastructure routing and fail-safe fallback mechanisms and relies on the Quantum Thought Circuit OS ASI architecture for deterministic decision control.
Jiayong Chai, Mo Chen, Wei Zhang, Xiaojuan Wang · 5 authors
Cross-domain data collaboration is a core requirement for the intelligent development of critical areas such as the Internet of Vehicles and intelligent transportation systems. In this scenario, vehicles and various sensors deployed roadside continuously generate massive amounts of time-series data, yet this data often forms "data silos" due to privacy regulations and a lack of trust between collaborating entities. Existing integrated schemes combining "Federated Learning + Blockchain" have achieved a certain degree of process traceability and automated payments, but risks of gradient-level privacy leakage persist, and inflexible and delayed incentive mechanisms result in low participation quality. To systematically address these bottlenecks, this paper proposes the Federated Learning with Assured Privacy and Reputation-Driven Incentives (FLARE) architecture, whose core innovation lies in the native integration of cryptographic security and mechanism design theory. It includes the Secure and Faithfully Executed Gradient aggregation (SafeGrad) protocol, which integrates partial homomorphic encryption and zero-knowledge proofs to provide verifiable privacy guarantees for gradient contributions while enabling efficient secure aggregation, defending against inversion attacks at the source; alongside this, it includes the Economy-on-Chain incentive (EconChain) mechanism, which designs an on-chain economic system based on blockchain, achieving precise measurement and sustainable incentivization of training process contributions through fine-grained instant micro-rewards and a dynamic reputation model. Experiments show that, compared to baseline schemes, FLARE can effectively enhance node participation enthusiasm and contribution quality without compromising model accuracy, providing a new paradigm with both strong security and high vitality for the trusted and efficient circulation of data.
Mar 2, 2026·Proceedings of the 2026 International conference on Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) solutions for Europe's Next-Gen Cloud Infrastructure
Nicola Bicocchi, Enrico Rossini, Marco Picone, Marco Mamei
The IoT-Edge-Cloud Continuum (IECC) demands data architectures capable of handling heterogeneity, distributed ownership, and governance across diverse stakeholders. This paper examines the combined use of Data Mesh and Data Spaces as complementary paradigms for addressing these challenges. Data Mesh decentralizes data management and computation across domains through autonomous data products; Data Spaces provide the trust, semantics, and policy frameworks required for sovereign and interoperable data exchange across organizations. Within the Horizon Europe NOUS project, we integrate these paradigms to form a knowledge-centric computing continuum. Using the Modena Automotive Smart Area (MASA) as a real-world testbed, we show how this integration supports scalable, trusted, and semantically aligned intelligence for smart mobility applications.
Longbo Han, Xiaodong Li, Lin You, Gengran Hu · 8 authors
Vehicular ad-hoc networks (VANETs) require authentication mechanisms that simultaneously deliver privacy, accountability, and timely cross-domain synchronization. The existing schemes struggle to balance unlinkable anonymity with effective tracing. They are also vulnerable to future quantum adversaries and rely on slow and costly revocation workflows. We present ZebraCPA, a decentralized conditional privacy-preserving authentication (CPPA) framework that combines lattice-based traceable ring signatures (TRS) with zero-knowledge (ZK) proofs and a consortium blockchain. Our TRS design removes linkability tags and embeds a tracing trapdoor only recoverable by the authorized auditors. It naturally extends to threshold tracing for multi-auditor settings. To avoid the plain-text key escrow, ZebraCPA leverages the additively homomorphic property of the commitments to support the ciphertext-only key updates by the vehicles, preventing the catastrophic key leakage at authorities. A hierarchical blockchain layer provides fast, consistent synchronization of active-key status across regions. The experiments show 1.7×–7.0× speedups over state-of-the-art baselines in signing/verification while retaining an anonymity-set size of N=10. The network-level simulations further indicate that ZebraCPA reduces an average packet delay by 30.7% - 61.6% compared with the baselines under moderate traffic densities. Moreover, the security of ZebraCPA is validated through our informal analysis under the Dolev-Yao model. Overall, ZebraCPA achieves post-quantum security, strong anonymity with conditional traceability, and practical deployment efficiency for VANETs, outperforming the existing solutions in terms of both latency and robustness.
Vehicular ad-hoc networks (VANETs) are networks based on short-range wireless communication technology, mainly used for direct communication between vehicles and interaction with roadside infrastructures. The emergence of VANETs has improved the efficiency and safety of vehicle travel. However, malicious vehicles may intentionally send incorrect messages to mislead other vehicles for personal gain, and this behavior cannot be identified yet. To address this issue, we propose an efficient, identifiable, and abortable multi-party signature scheme for VANETs. Specifically, we utilize the property of zero-knowledge proofs to design a method that can efficiently identify malicious vehicles during the signature process, and define a strategy to quickly locate false proofs. Additionally, the message signing key in the proposed scheme is jointly generated by multiple entities, which avoids the security issues of key escrow and single point failure. Through rigorous security analysis, it is demonstrated that the proposed scheme satisfies essential security requirements for VANETs, including message unforgeability and authentication, vehicle anonymity, malicious traceability, collusion resistance, replay attack resistance, identifiable abort, and forward security of signing private key. Performance analysis shows that our scheme reduces the total aggregation verification time by up to 80% compared to existing schemes, and maintains a lower packet loss rate ($< $0.6%) at higher vehicle densities. Therefore, our scheme is suitable for large-scale VANETs deployments.
Xuehan Li, Tao Jing, F. Richard Yu, Hongwei Wang · 9 authors
Connected and autonomous vehicles (CAVs) enhance traffic efficiency and safety via massive data-driven computation and decision-making. The computational demands of massive data challenge centralized cloud networks, leading to a novel CAV paradigm supported by mobile edge computing (MEC) and built on Web3. CAVs in Web3 can efficiently and securely offload compute-intensive tasks to edge devices in a decentralized and self-controlled manner, necessitating dependable task offloading schemes. However, existing deep reinforcement learning (DRL)-based offloading schemes face two challenges: overlooking security risks like privacy exposure in dependability definitions, while being constrained by reward function formulation, resulting in poor generalization. In this paper, we propose a dependable offloading scheme based on intelligence and active inference for CAVs in Web3. First, we introduce a dependable offloading framework utilizing double-layer blockchain and decentralized identifiers to ensure offloading source dependability. Then, by introducing a security-measuring dependability metric called cost from energy consumption, delay, and privacy exposure risk (cEDP), we formulate the dependable offloading optimization problem from an intelligence and active inference perspective, enabling higher-level environmental cognition without rewards. The problem is solved by the proposed intelligence-based active inference (INAI) algorithm. Experimental results demonstrate that reward-free INAI outperforms mainstream DRL and heuristic approaches in convergence, efficiency, and generalization capabilities.
The presence of shared micro-vehicles, such as bicycles and e-scooters, has become increasingly common in modern urban environments, enhancing citizens’ access to public transportation by providing an efficient solution to the last-mile problem. In recent years, shared mobility has expanded to include larger vehicles, such as cars and sea vessels, facilitating transportation over longer distances and offering an alternative to private and public modes of transport. However, the seamless integration of these different transportation modes remains a significant challenge, as each type of vehicle has its own advantages and limitations. Furthermore, these transport services are often operated by different organizations that use distinct platforms and ticketing systems, further complicating coordination among them. In this work, we present the proposed approach and the developed system designed to facilitate the adoption and integration of different types of vehicles using AI and blockchain technologies. The system enables users to identify and utilize the most appropriate means of transport through a unified, blockchain-based mechanism. Preliminary evaluation results, based on simulated data, indicate that the system can significantly benefit citizens in a smart city environment and, when combined with appropriate investments in urban infrastructure, can substantially improve daily mobility.