The rapid growth of Industrial Internet of Things (IIoT) systems has heightened the need for secure cryptographic operations, particularly multi-party digital signatures for decentralized trust. However, existing multi-party signature schemes are vulnerable to insider attacks, and no current solutions address insider-induced data exfiltration effectively. Cryptographic Reverse Firewalls (CRFs) provide a promising solution but face challenges in integration with digital signatures, especially with hash-dependent components. We propose MCRF, a CRF-enhanced multi-party signature scheme designed for IIoT environments. MCRF uses a commitment-based mechanism to optimize the signing process, enabling output-side CRF to re-randomize signatures without compromising correctness. The two-stage CRF architecture-input-side CRF for sanitizing messages and output-side CRF for re-randomizing signatures-ensures efficient protection against both input-triggered and output-stealth exfiltration attacks. MCRF offers strong leakage resistance with minimal computational and communication overhead, making it a scalable solution for secure multi-party signing in IIoT systems.

As a promising subdomain of intelligent transportation systems (ITS), Internet of Vehicles (IoV) can be empowered by digital twin (DT) technology for real-time traffic simulation and artificial intelligence (AI)-driven predictive analytics in evolutionary trend projection, demonstrating significant potential in dynamic transportation optimization. Among various machine learning paradigms, federated learning (FL) not only aligns well with IoV, but also provides it with privacy protection. Traditional FL faces single point of failure due to the existence of an aggregation center, so blockchain-based FL with multiple aggregators is utilized to mitigate this issue. Nevertheless, in such distributed environments, both aggregators and model parameters exposed to network are vulnerable to attacks, impeding the normal operation of FL. In this paper, for blockchain-enabled FL with multiple aggregators in IoV, we propose CLTSwNI&IA, the first non-interactive certificateless threshold signature with identifiable abort. This scheme eliminates certificate management and key escrow, adopts a blockchain-oriented approach by utilizing a fully distributed signing paradigm. Additionally, the proposed signing scheme is capable of identifying malicious FL aggregators during the entire process through distributed fine-grained verification and ensuring the integrity of aggregation results. Finally, theoretical and experimental comparisons with related literature demonstrate the advanced functionality and the acceptable efficiency of our approach.

As the increasing number of diabetic around the world and the deficiencies of invasive blood glucose detection, such as prickly pain, risk of infection, and no continuous monitoring, a proper noninvasive blood glucose monitoring method is highly desired to deal better with it. In this article, an ultrawide band microwave based technique combined with a cascaded general regression neural network (C-GRNN) is proposed for blood glucose level estimation to achieve noninvasive and intelligent monitoring. Different glucose solutions are tested by using the reported monitoring system in which C-GRNN is used for intelligent detection and frequency choice. The proposed C-GRNN can reduce the prediction interval from 12.3858 to 2.1375 mg/dL compared with the conventional GRNN, and the root-mean-square error (RMSE) reaches 2.20 mg/dL. These results show good estimation performance. We also conduct a continuous-time blood glucose level monitoring on volunteer using the proposed noninvasive method. The final results show well accuracy in which the RMSE and mean absolute percentage error are 13.3208 mg/dL and 9.74%, respectively.

The miniaturized electronic products require not only high density, but also high reliability. Defect inspection for the high-density package becomes challenging gradually. In this article, active thermography technology is used to detect the solder balls in flip-chip (FC) packages. A single-layer FC model is constructed and heat conduction in the FC package is simulated by using the finite-element analysis code of COMSOL. Experimental investigation is also carried out to inspect the solder defects by using the laser excitation thermography test system. Infrared images of the SFA1 package are captured, and the spatial adaptive filtering algorithm is used to remove the thermal noise. The hot spots of the solder balls are segmented from the filtered infrared image, and the representative features are extracted for each hot spot. The modified learning vector quantization (LVQ) neural network is used for the classification of the solder balls. The missing solder balls are identified accurately. The simulation and experimental results prove that the proposed approach using active thermography and LVQ algorithm is effective for defect inspection in high-density electronic devices.