In the era of quantum computing, data sharing in the Internet of Vehicles (IoV) confronts the challenges of auditability, efficiency, and quantum security. However, existing research remains insufficient to meet the requirements of high mobility, resource constraints, and resilience against quantum attacks. In this paper, we propose a new quantum-secure auditable data sharing framework, in which we first present a quantum-resistant puncturable signature algorithm (QRPPRFS). Combining the low-noise LPN-based pseudorandom function with an optimized trapdoor generation mechanism, it achieves compact key sizes and millisecond-level signing; second, the blockchain and dual-commitment proof mechanism are integrated to ensure anonymity, transparent auditability and robustness. Finally, we rigorously demonstrate the correctness of our scheme, the EUF-CMA with puncturing of QRPPRFS, and the knowledge soundness and witness zero-knowledge of the dual-commitment proof system. Experimental evaluations show that, under the practical setting$n=256$and$q \approx 2^{23}$, the proposed scheme keeps both signing and verification latencies below 10 ms, and reduces the initial secret-key storage to only 0.22 MB. These results demonstrate that the proposed scheme achieves both enhanced security and high efficiency, outperforming existing schemes.

Redactable blockchains are challenging the core principle of traditional blockchains: immutability. One such example is the chameleon hash-based blockchain. Despite rapid academic advances, most solutions have not yet simultaneously considered four key aspects: the degree of modification privileges, the transparency of the modification process, the consistency in the post-redaction global state, and system security after redaction. In this paper, we present a controllable, publicly auditable, and redactable blockchain with a main-auxiliary architecture. Specifically, we integrate weighted secret sharing, digital signature, and non-interactive zero-knowledge proof technologies to propose a verifiable and controllable chameleon hash primitive. To encourage logical nodes, it includes a reputation evaluation mechanism and a DAO-based governance model. Additionally, we construct a redactable bi-directionally anchored main-auxiliary blockchain structure, where the auxiliary chain exclusively maintains the modification proofs associated with each block of main chain. Any node can audit the modification history or, in the event of an accusation, self-prove. This structure also simplifies global state updates for newly joined or restarted nodes. Finally, we provide comprehensive security proofs for our construction, conduct extensive experiments to evaluate its functionality and performance, and compare it with analogous solutions to demonstrate its superiority.

Blockchain transaction privacy is a highly researched topic across various application scenarios. Current privacy-preserving schemes in blockchain employ advanced cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, to balance transaction privacy with regulatory requirements. However, these schemes encounter challenges, including computational inefficiency, data expansion, and overlooked metadata privacy, such as timestamp protection. In this paper, we first propose a privacy-enhanced traceable anonymous transaction scheme based on data transaction scenarios. This scheme integrates ring signature and Merkle hash tree techniques, effectively shortening the signature size and optimizing the verification process compared to existing combinations of ring signatures and zero-knowledge proofs. A novel verifiable timestamp privacy protection method is introduced, which obfuscates timestamps to prevent tampering without compromising integrity. To enhance scalability, this method extends to multiple transaction processing scenarios and implements a timestamp-sharing strategy to reduce the computational burden. It also allows tracking authorities to monitor the long-term addresses of both transaction parties if necessary. Rigorous security analysis and extensive experimental evaluations demonstrate that this scheme achieves superior privacy, traceability, and scalability compared to existing approaches.

The integration of Internet of Things (IoT) with cloud–edge computing in cyber–physical systems has revolutionized the way healthcare enterprises manage electronic health records (EHRs). With more healthcare enterprises outsourcing encrypted EHRs to the cloud, searchable encryption (SE) is utilized to retrieve encrypted data, especially attribute-based SE (ABSE) can achieve fine-grained access control. However, ABSE usually requires a lot of computation, which imposes a serious burden on resource-limited devices. Moreover, ensuring fairness in data access is crucial in the healthcare domain, where both data users and owners may have conflicting interests. In order to overcome these problems, this article proposes an SE scheme with fine-grained access control for cloud-based EHRs sharing assisted by blockchain. It transfers computing tasks to edge servers and enables users to control who has access to their EHRs. The adoption of blockchain and smart contracts guarantees data integrity and transaction fairness. Moreover, a consensus algorithm is designed for the higher efficiency of the proposed scheme. Finally, security analysis proves that the proposed scheme resists adaptive chosen keyword attacks (CKAs). Performance analysis further confirms that it has more functionalities and is efficient for smart healthcare.

With the maturity of deep learning algorithm in Synthetic Aperture Radar (SAR) target recognition filed, Convolutional Neural Network (CNN) has become the most effective model. However, the interpretability and the separability of feature maps extracted from convolution layers have not been specially analyzed neither qualitatively nor quantitatively, which makes the traditional model work like a “black box”. To alleviate the problem, a novel model based on separability measure (SM) - CNN is proposed in this letter, which introduces the principle of maximal coding rate reduction to the backbone module. SM-CNN quantitatively analyzes the separability of the feature maps and takes the value as a vital part of the loss function to guide the training process of the model. The calculation process of the separability measure values can be strictly derived mathematically, so it is more interpretable, turning the black box into a “gray box”. Additionally, the proposed model can achieve comparable recognition performance of the backbone networks with reduced computational complexity. Comparative experiments based on MSTAR and OpenSARShip data sets verify the effectiveness and practicability of the method proposed in this letter.

The Augmented Intelligence of Things enables many edge or end devices in the Internet of Things (IoT) to perform machine reasoning to make decisions, thus become more intelligent. For healthcare enterprises, huge physical data generated by smart devices facilitate to iterate their products. However, traditional data sharing models based on cloud outsourcing meet many security challenges, such as data confidentiality, reliability, and privacy protection, and most existing schemes have high computational complexity for the utilization of time-consuming cryptographic operations, such as bilinear pairing, which is not suitable for those resource-constrained IoT devices. To tackle the abovementioned issues, we present Double Rainbows, a promising data sharing scheme based on pairing-free searchable encryption. It is constructed on the cloud-edge-end architecture, supporting computing task transfer and reliable data storage and retrieval. The experimental results show that it outperforms in efficiency and exhibit more security functionalities.

Regional medical consortium systems facilitates medical information sharing. However, many security issues exposed by the dominant centralized architectures, such as single points of failure, unauthorized operations and illegal access, are increasingly apparent constraints on the security and efficiency of data sharing across domains. Even more, any malicious operation detected, effective measures should be executed promptly for identity tracing. In this paper, we propose a secure and efficient cross-domain authentication scheme based on two cooperative blockchains (BCs) for medical consortium systems. Specifically, an intra-domain BC records any legal users’ registration and authentication information while an inter-domain BC is responsible for writing users’ cross-domain authentication information. In each domain, the general hospital acts as a trusted third service provider to achieve cross-chain interactions. For the entire cross-domain authentication procedure, anonymity mechanism is utilized to enhance security, and to trace malicious users, the improved chameleon hash is used in the intra-domain BC to redact the state of the user, and blacklist merkle tree is extended in the inter-domain BC to protect different domains’ services from illegal accessing. In addition, security analysis and performance evaluation are completely given to prove the superior security features and performance compared with other schemes.

The Internet of Things (IoT) has built an information bridge between people and the objective world, wherein wireless sensor networks (WSNs) are an important driving force. For applications based on WSN, such as environment monitoring, smart healthcare, user legitimacy authentication, and data security, are always worth exploring. In recent years, many multifactor user authentication schemes for WSNs have been proposed using smart cards, passwords, as well as biometric features. Unfortunately, these schemes are revealed to various vulnerabilities (e.g., password guessing attack, impersonation attack, and replay attack) due to nonuniform security evaluation criteria. Wang et al. put forward 12 pieces of widely accepted evaluation criteria by investigating quantities of relevant literature. In this paper, we first propose a lightweight multifactor authentication protocol for multigateway WSNs using hash functions and XOR operations. Further, BAN logic and BPR model are employed to formally prove the correctness and security of the proposed scheme, and the informal analysis with Wang et al.’s criteria also indicates that it can resist well-known attacks. Finally, performance analysis of the compared schemes is given, and the evaluation results show that only the proposed scheme can satisfy all 12 evaluation criteria and keep efficient among these schemes.