Edge nodes in the Industrial Internet of Things (IIoT) often face a fundamental trade-off between limited computational resources and stringent real-time inference requirements. Moreover, sensitive data they generated are exposed to significant privacy and security threats during transmission and computation. To address these challenges, this paper proposes a lightweight additive homomorphic edge neural network framework called LAHENet. This framework achieves millisecond-level inference latency in real-world industrial environments through a combination of a dual-metric feature selection strategy, an efficient additive homomorphic signcryption protocol, and a lightweight linear computation layer with adaptive layer collapsing. It ensures end-to-end confidentiality, unforgeability, forward security, and verifiable computation correctness. Experimental results show that LAHENet maintains a constant communication overhead at a few kilobytes per inference while preserving high model accuracy. It significantly enhances inference efficiency and reduces bandwidth consumption in edge environments, offering a practical private inference solution for large-scale IIoT deployments.

With the growth of the Industrial Internet of Things (IIoT), millions of smart devices are transmitting and processing data globally. However, this extensive interconnectivity also poses significant security challenges, particularly in data transmission. Traditional security mechanisms often incur high computational costs and long processing times, which are impractical for resource-constrained devices. In this paper, we propose an efficient and secure data processing and transmission scheme for the IIoT called ECGSH. This scheme combines certificateless signcryption and homomorphic encryption to enable homomorphic processing in an encrypted state, thus enhancing both security and flexibility. Moreover, it reduces the complexity of large-scale data processing by eliminating bilinear pair computations. The ECGSH scheme also supports homomorphic data transmission in the IIoT. A rigorous security analysis proves that the scheme has the properties of confidentiality, non-repudiation, and forward security under the random oracle model. An attack resistance analysis proves that the scheme can effectively resist man-in-the-middle (MITM) attacks, replay attacks, and eavesdropping attacks. The performance evaluation demonstrates that ECGSH excels in terms of security, computational efficiency, and communication overhead. It requires at most 31% CPU utilization, and less than 1.2% memory footprint on IIoT hardware, making it particularly suitable for IIoT environments with limited resources and high transmission costs.

Inertial measurement unit (IMU) fingerprinting is a promising physical authentication technique based on hardware imperfections produced during sensor manufacturing. This paper presents a two-stage feature extraction process that combines feature selection and mapping; the proposed approach is tailored for the lightweight vehicle-to-everything (V2X) application scenario. Specifically, the selected features are transformed into images via Gramian angular difference field (GADF), Gramian angular summation field (GASF), and Markov transition field (MTF) mappings, as well as feature extraction implemented via a convolutional neural network (CNN). Owing to the advances provided by the proposed scheme, a lightweight feature extraction system achieves satisfactory accuracy levels above 99.10% with fewer sample data and a short training time. The effectiveness and robustness of the developed approach were validated under various driving conditions via 20 IMU sensors, Arduino, and a Raspberry Pi across 20 vehicles. Additionally, tests conducted across different deep learning models demonstrated the generalizability of the proposed preprocessing and mapping methods.

Attribute-Based Signature (ABS) provides a critical solution for ensuring data integrity, fine-grained access control, and anonymous authentication in security-sensitive systems such as the Multimedia Internet of Things (MIoT) and multimedia streaming platforms. However, practical adoption of ABS faces three fundamental challenges: vulnerability to key exposure and escrow risks, linear growth of computational cost, and insufficient robustness in multi-authority environments. To address these issues, we propose a forward secure and threshold authorized multi-authority ABS scheme called FORT in this paper. By employing a binary tree structure to divide multiple time periods, historical signatures remain valid even in the event of key exposure. Furthermore, to balance robustness and resistance to corruption while mitigating the key escrow problem, we construct a threshold authorized multi-authority structure based on Lagrange interpolation. This structure effectively reduces the impact of a single authority on the MIoT. Additionally, through the adoption of outsourced computation technology, which offloads complex computations in the signature and verification phases to the edge server, the computational burden for both the signer and verifier is significantly reduced to a small constant. Rigorous security analysis demonstrates that the FORT scheme achieves forward security, collusion attack resistance, corrupt authority resistance and anonymity. Theoretical comparisons and simulation experiments demonstrate the lightweight nature of the FORT scheme in terms of computation and communication.