Route planning helps a vehicle to share a message with the roadside units (RSUs) on its path in advance, which greatly speeds the authentication between the vehicle and the RSUs when the vehicle enters the RSUs’ coverage. In addition, since only a small amount of necessary information needs to be shared between the vehicle and the RSUs, route planning can reduce the storage overhead of the vehicle’s on-board unit (OBU) and the RSUs. However, the message sharing requires the assistance of the certification authority (CA), which will lead CA easily to obtain the vehicle’s planning route. Although CA knows the vehicle’s registration information and helps the vehicle to communicate with RSUs, it is unacceptable that the path of their vehicle is obtained by CA for most drivers. In fact, vehicle’s sensitive information such as planning route, starting time, stop place, should be privacy for others including CA. Inspired with the method of oblivious transfer, a preserving-privacy route planning scheme in VANETs is proposed in this article, in which, a vehicle deduces the information of RSUs on its path with the help of CA, while CA knows nothing about which RSUs’ information has been deduced by the vehicle. Later, fast authentication or other service is easily achieved between the vehicle and the RSUs (V2R) with the pre-shared information. After V2R authentication, vehicles could easily communicate with adjacent vehicles with the help of RSUs (V2V). Finally, compared with related schemes, performance evaluation illustrates the proposed scheme is better in terms of time consumption.

To support comprehensive and personalized healthcare delivery, sensitive personal health data must often be transmitted from wearable devices to multiple designated specialists via wireless body area networks. Although existing identity-based broadcast signcryption schemes provide secure one-to-many transmission, they typically fail to preserve recipient anonymity. In particular, each recipient is usually required to know the identities of all other recipients to unsigncrypt the message, which undermines privacy and enables inference attacks, especially problematic in telemedicine applications. In this paper, we propose a novel broadcast signcryption scheme that simultaneously achieves recipient anonymity, sender anonymity, and resilience to physical attacks, which are not realized in prior work. Our key innovation lies in an anonymity-preserving unsigncryption mechanism based on inner product, which enables each recipient to independently verify and decrypt the broadcast message without knowledge of others’ identities. In addition, we incorporate a physical unclonable function with a fuzzy extractor to resist physical attacks in the presence of noise. Furthermore, a comprehensive security analysis demonstrates that our scheme satisfies essential security properties, including authentication, integrity, confidentiality, and full anonymity. It is resilient against common attacks such as impersonation, modification, replay, and man-in-the-middle attacks. Finally, performance evaluations confirm the practical feasibility of our scheme.

Vehicle-to-Grid (V2G) networks must exchange sensitive data across open wireless links, yet existing Authentication and Key Agreement (AKA) schemes remain vulnerable to computational overload, single-factor compromise, physical tampering, and de-synchronization. We propose a lightweight multi-factor AKA scheme that overcomes these limitations while matching the tight resource budget of on-board units and Charging Stations (CSs). The design uses only inexpensive primitives, hash, XOR, Physical Unclonable Function (PUF), and fuzzy extractors, so every cryptographic step can be executed on low-end controllers. Four factors (identity, password, biometrics, and PUF entropy) are jointly bound, guaranteeing that impersonation remains infeasible even if an attacker compromises up to $n\!-\!1$ factors. To ensure physical security with modeling resistance, the scheme avoids storing raw PUF responses on the device. Instead, it retains only the challenge and helper data, embedding the extracted secret within non-invertible hash functions. This strategy thwarts state-of-the-art modeling attacks that rely on harvesting large challenge-response datasets, while fuzzy extractors absorb PUF noise and preserve reliability without auxiliary hardware. Synchronization is maintained through a dual-state cache $\lbrace S^{\rm{old}}, S^{\rm{new}}\rbrace$ kept on both Electric Vehicles (EVs) and CSs, so legitimate nodes remain aligned even if an adversary suppresses in-flight messages. Comprehensive security analysis, security verification, and performance evaluation show that the proposed scheme achieves mutual authentication, forward-secure key agreement, password friendliness, conditional anonymity, unlinkability, and resistance to guessing, replay, physical, modeling, and de-synchronization attacks, at a lower computation and communication cost than recent alternatives.

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL’s privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads.