The data generated in the Internet of Things (IoT) holds significant transactional and utilitarian value. However, traditional data trading models face numerous challenges, including privacy leaks of raw data and limitations in arbitration. This paper proposes a secure and verifiable privacy-preserving data trading scheme for IoT (SVDT), in which perturbed data is substituted for raw data to fundamentally mitigate the risk of privacy leakage. Secondly, we construct a dual-verification mechanism based on homomorphic encryption and merkle trees to simultaneously achieve verifiability and confidentiality during the trading process. Additionally, smart contract facilitate escrow and automated execution to ensure trading fairness. Finally, a collusion-resistant anonymous arbitration mechanism is designed utilizing ring signatures to sever the association between the arbitrator’s identity and the adjudication result, thereby safe-guarding the anonymity and independence of the arbitration while effectively resolving disputes. Theoretical analysis demonstrates that the scheme effectively achieves data security and trading fairness, with security analysis validating its privacy-preserving properties. Experimental results show a favorable balance between performance overhead and privacy utility. The findings indicate that SVDT scheme provides a practical solution for fair trading involving IoT data.

Wireless medical sensor networks (WMSNs) have been widely adopted in healthcare for collecting users' physiological data, providing crucial references for medical diagnosis and prevention. However, transmitting sensitive data over public networks faces security risks, potentially leading to privacy breaches and financial losses. Moreover, large-scale data transmission increases energy consumption, hindering continuous monitoring. Therefore, achieving energy efficiency alongside data security is critical for WMSNs. This paper proposes a lightweight slope-based piecewise linear approximation algorithm for online data compression, utilizing slope intervals under a user defined error bound, to reduce energy consumption. Concurrently, we introduce a pairing-free certificateless aggregate signature scheme, proven secure under the random oracle model against different type adversaries, to enhance data privacy and integrity. Experimental results demonstrate that the compression algorithm achieves efficient compression while preserving trends, and the aggregate signature scheme reduces computational overhead by 20% without increasing communication costs.

The rapid proliferation of the Internet of Things (IoT) has made resource exchange and collaboration across diverse IoT domains commonplace, necessitating secure and privacy-preserving cross-domain authentication. However, existing schemes suffer from critical limitations: they lack time-bound access control, leading to persistent unauthorized access and heightened security risks, and most are incomplete, requiring resource-intensive redeployment of cryptographic mechanisms and increasing management overhead. To address these challenges, we propose a dynamic time-bound anonymous complete cross-domain authentication scheme that leverages consortium blockchain for decentralized trust, embeds dual temporal constraints, expiration time and permissible authentication periods, into credentials for fine-grained access control and automatic natural revocation, and employs accumulators and non-interactive zero-knowledge proofs (NIZKs) to enable anonymous authentication while ensuring strong privacy protection. Crucially, the proposed scheme achieves complete cross-domain authentication without modifying existing cryptographic mechanisms, significantly reducing overhead in computational, communication, and storage. Security and performance analyses confirm that the proposed scheme not only guarantees robust security and privacy but also outperforms existing schemes in efficiency.

Cloud-fog automation architecture has propelled the advancement of the Industrial Internet of Things (IIoT), significantly enhancing production efficiency and intelligence through extensive data collection and connectivity. Simultaneously, industrial cyber-physical system leverages this data to achieve intelligent control and optimization of production processes. As industrial production becomes increasingly specialized and complex, independent operations within a single domain are no longer sufficient to meet demands, making cross-domain collaborative production inevitable. Consequently, ensuring the security of cross-domain communication and data sharing has become a critical issue for IIoT under the cloud-fog automation architecture. Existing solutions encounter substantial management and computational burdens in cross-domain communication and data sharing, and they are vulnerable to privacy leakage risks. To address these challenges and enhance industrial production efficiency, this paper uses consortium blockchain to co-design a cross-domain authentication and data sharing scheme. The scheme ensures secure and private cross-domain communications with minimal computational, communication, and storage overhead. And, the proposed time-specific plaintext checkable encryption protocol can secure data during cross-domain sharing. Security and performance analyses show that the proposed scheme effectively reduces computational and communication resource demands while maintaining communication and data security.

A multi-feature broad learning system (MFBLS) is proposed to improve the image classification performance of broad learning system (BLS) and its variants. The model is characterized by two major characteristics: multi-feature extraction method and parallel structure. Multi-feature extraction method is utilized to improve the feature-learning ability of BLS. The method extracts four features of the input image, namely convolutional feature, K-means feature, HOG feature and color feature. Besides, a parallel architecture that is suitable for multi-feature extraction is proposed for MFBLS. There are four feature blocks and one fusion block in this structure. The extracted features are used directly as the feature nodes in the feature block. In addition, a “stacking with ridge regression” strategy is applied to the fusion block to get the final output of MFBLS. Experimental results show that MFBLS achieves the accuracies of 92.25%, 81.03%, and 54.66% on SVHN, CIFAR-10, and CIFAR-100, respectively, which outperforms BLS and its variants. Besides, it is even superior to the deep network, convolutional deep belief network, in both accuracy and training time on CIFAR-10. Code for the paper is available at https://github.com/threedteam/mfbls .