The information sharing among internet-connected vehicles is close, but there may be the issue of vehicles providing false information. This paper proposes a new incentive mechanism scheme for VANET to provide a decentralized and trusted service management. We introduce a trust-based message evaluation system, incorporating reputation assessment and negative feedback functions to evaluate the credibility of messages and assessments. A reward and punishment mechanism based on repeated games is designed to incentivize vehicles to proactively share information, thereby preventing false information and defamation attacks. Additionally, a blockchain-based distributed trust management scheme is designed to ensure the consistency of trust management storage through the consensus mechanism of the blockchain, reducing reliance on centralized institutions. Simulation results demonstrate that the establishment of reputation and incentive mechanisms among vehicles enhances the enthusiasm for information sharing and improves the accuracy of identifying false messages.

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access.

In the Internet of Things (IoT), data sharing security is important to social security. It is a huge challenge to enable more accurate and secure access to data by authorized users. Blockchain access control schemes are mostly one-way access control, which cannot meet the need for ciphertext search, two-way confirmation of users and data, and secure data transmission. Thus, this paper proposes a blockchain-aided searchable encryption-based two-way attribute access control scheme (STW-ABE). The scheme combines ciphertext attribute access control, key attribute access control, and ciphertext search. In particular, two-way access control meets the requirement of mutual confirmation between users and data. The ciphertext search avoids information leakage during transmission, thus improving overall efficiency and security during data sharing. Moreover, user keys are generated by the coalition blockchain. Besides, the ciphertext search and pre-decryption are outsourced to cloud servers, reducing the computing pressure on users and adapting to the needs of lightweight users in the IoT. Security analysis proves that our scheme is secure under a chosen-plaintext attack and a chosen keyword attack. Simulations show that the cost of encryption and decryption, keyword token generation, and ciphertext search of our scheme are preferable.

Semi-supervised learning (SSL) is critical for medical image segmentation but often struggles with network dependency and pseudo-label error accumulation. To address these issues, we propose a fractal-dimension-guided hierarchical contrastive learning dual-student framework(FD-HCL). We extend the Mean Teacher architecture with a dual-student design and introduce an independence-aware exponential moving average (I-EMA) update mechanism to mitigate model coupling. For enhanced feature learning, we devise a hierarchical contrastive learning (HCL) mechanism guided by voxel uncertainty, spanning global, high-confidence, and low-confidence regions. We further improve structural integrity by incorporating a fractal-dimension (FD)-weighted consistency loss and integrating a novel uncertainty-aware bidirectional copy–paste (UB-CP) augmentation. Extensive experiments on the LA and BraTS 2019 datasets demonstrate the state-of-the-art performance of our framework across 10% and 20% labeled data settings. On the LA dataset with 10% labeled data, our method achieved a Dice score that outperformed the best existing approach by 0.68%. Similarly, under the 10% labeling setting on the BraTS 2019 dataset, we surpassed the state-of-the-art Dice score by 0.55%.

The Internet of Things (IoT) has expanded rapidly, generating vast amounts of data that require effective protection. Traditionally, the attribute-based searchable encryption (ABSE) as been used within IoT to maintain data privacy and integrity. However, existing ABSE approaches often rely on centralized systems that are susceptible to single-point failures and are inefficient in terms of decryption and data retrieval. Therefore, we propose a novel blockchain-assisted multi-keyword searchable encryption scheme (BAMKSE) that utilizes the decentralized nature of blockchain to overcome these limitations. This scheme integrates ciphertext policy attribute-based encryption (CP-ABE) with key policy attribute-based encryption (KP-ABE) to provide advanced access control and mutual authentication, while offloading the computational burden of decryption to cloud-based pre-decryption services. Security analyses demonstrate the resilience of BAMKSE against chosen keywords attacks (CKAs), offering an effective solution for secure and efficient IoT data handling.