Blockchain transaction verification is fundamental to decentralized financial applications, ensuring both transaction authorization and integrity. Most existing verification schemes utilize an equal weight model that grants identical rights to all participants. However, these schemes fail to capture real-world scenarios like Proof-of-Stake blockchains, where participants have right discrepancies. Additionally, many schemes depend on trusted third parties for key generation, introducing single points of failure and compromising security. To address these limitations, we propose WBlock that is a weighted and decentralized verification scheme. WBlock involves a distributed key generation protocol that embeds each entity's weight into its secret share, eliminating the need for trusted third parties. It further employs a Schnorr-type weighted threshold signing protocol, enabling distributed transaction authorization while reflecting participant weight in signature shares. Security analysis shows that WBlock achieves both correctness and unforgeability. Comparative theoretical analysis shows that our key generation and signing protocols outperform existing methods in terms of round complexity, communication overhead and data size. Experimental results confirm that WBlock achieves a balance between security and efficiency, with acceptable overhead for real-world blockchain deployment.

Artificial intelligence (AI) revolutionizes the productivity model and efficiency of the Industrial Internet of Things (IIoT). As a derivative of the AI era, AI-enabled IIoT drives frequent data access and intelligent industrial productivity. However, the rise of intelligence brings more sophisticated and hard-to-defend attacks against IIoT systems, such as deep identity forgery and malicious access, posing a major threat to intelligent development. Password-based Authenticated Key Agreement (AKA) is an effective cryptographic method for access security in IIoT, but current AKA schemes cannot address balancing between security, functionality and efficiency in the smart setting. To fill this gap, we propose a new password-based AKA scheme, where oblivious pseudorandom function, hash function and encryption are utilized to realize anonymous identity authentication. Considering malicious data access, we design a new token-tag mechanism with identity information to realize malicious identity tracing. In addition, our scheme supports a fast login function, helping the authorized party access data without repeating key agreements. Furthermore, formal security proofs and heuristic analyses demonstrate that our scheme is secure under multiple attacks. Finally, we compare the proposed scheme with the related schemes, and the results show that our scheme achieves the balance between safety, function and efficiency.

Hyperspectral images provide rich spectral information and have been widely applied in numerous computer vision tasks. However, their low spatial resolution often limits their use in applications such as image segmentation and recognition. In previous works, generating high-resolution hyperspectral (HR-HS) images required the use of low-resolution hyperspectral (LR-HS) images and high-resolution RGB (HR-RGB) images as priors, which increases the cost of data collection and may lead to measurement and calibration errors in practical applications. Although the currently popular CNN-based single hyperspectral image super-resolution (single HS-SR) methods have improved performance, they are not flexible enough to process images with different degradation. From a visual perspective, the generated super-resolution images exhibit a significant smudging effect due to the loss of information. Leveraging multi-modal techniques and generative prior, we propose DiffHSR that marks a significant leap in LR-HS images super-restoration without HR-RGB. Additionally, we have established a connection between hyperspectral images and the RGB image-based generative model tasks using low-cost data and fine-tuning approaches, which creates a novel paradigm. Comprehensive experiments have demonstrated that our proposed method achieves strong visual performance and competitive results in term of quantitative metrics and perceptive quality.

Threshold signatures as a method to realize multi-party cooperation and trust distribution in blockchain have been widely studied in recent years. However, among these researches, few threshold signature schemes achieve all the properties of accountability, privacy, and key protection for the EdDSA-based blockchain systems. To fill this gap, we propose an EdDSA-based accountable threshold signature protocol with privacy and proactive refresh, named TAPS-PR. Meanwhile, we define new security models and give a detailed analysis to prove protocol security. In TAPS-PR, the threshold is variable and hidden with the signing quorum from the public view. However, the signing quorum can be traced when threshold signatures related to fraudulent events are generated. We also enhance the key security of each signer by proactive refresh, which realizes updating the private key while the public key remains unchanged. Apart from that, we present ATS-PR with increased efficiency and reduced communication cost at the cost of weaker security. The theoretical analysis and experimental results indicate that our protocols perform efficiently in terms of communication and computation overhead. Furthermore, we use Tezos, a blockchain project employing EdDSA, as a case study to demonstrate the compatibility of our protocol with real-world blockchain applications.

The e-health system enables online healthcare by supporting health data transmission services on medical platforms. Considering the frequent privacy breaches in e-health systems and the issuance of relevant regulations, it is important to ensure the authenticity and privacy of health data. Existing e-health systems either fail to provide data authenticity or neglect privacy protection after patients leave the system. In this article, we put forward a secure and efficient e-health system for data transmission, named PPED, to solve this dilemma. In PPED, we explore a regular signature and a forward-secure signature, which guarantee data authenticity and give the signature a valid period. Then, a specific epochal signature scheme is designed by combining two signature schemes with the time-lock puzzle. Since expired epochal signatures are forgeable, patients after leaving the e-health system can forge expired signatures to deny their relationship with the signed data, thus achieving privacy protection. Detailed security analysis demonstrates the PPED realizes data authenticity and user privacy. Extensive experiments evaluate our system and the results show it is practical in terms of running time.