Smart grids have revolutionized electricity management by leveraging real-time consumption data, enabling more efficient power control through advanced algorithms. However, this transformation raises significant privacy and security concerns due to the extensive collection of user data. Current solutions face challenges, such as aggregator gateway misbehavior, lack of support for function queries, and the need to balance privacy with efficiency. In this article, we propose FEHH, a novel scheme that ensures both privacy preservation and verifiable aggregation. It allows multiple aggregators to perform inner-product computations on encrypted data while safeguarding the aggregated results from the aggregator. Additionally, it supports verification of aggregated data’s correctness using Linear Homomorphic Hash. Building on FEHH, we introduce GuardGrid, a privacy-preserving aggregation scheme for smart grids that inherits FEHH’s core features and adds support for essential arithmetic operations necessary for function queries. This allows cloud servers to respond to queries from either the control center or users without compromising data confidentiality. Experimental results show that the encryption overhead of GuardGrid is only 7% of that of the PPDA scheme, and its communication overhead is$123\times $less. These results demonstrate that GuardGrid significantly reduces computation and communication costs, providing a more sustainable and cost-effective smart grid solution.

This paper investigates a fixed-time consensus control problem of nonlinear multi-agent systems under full-state constraints. First, by designing corresponding constraint functions for system transformation, state-dependent asymmetric time-varying constraints are realized. The feasibility conditions of the system are eliminated, and the requirements on the constraint boundary are relaxed. Meanwhile, a prescribed performance function is designed for the constraints on synchronization deviation, which helps to improve the transient and steady-state performance of the system and ensure rapid consensus convergence on a fixed-time framework. Additionally, considering the frequent communication between the controller and actuator and to decrease the controller update frequency to save system bandwidth, an adaptive threshold event-triggered mechanism is developed. A dynamic parameter is introduced into the triggered mechanism to adjust the triggered threshold, thereby overcoming the issue that static parameters might cause excessive or insufficient event-triggered and avoiding Zeno behavior. Finally, the effectiveness of the proposed strategy is verified through simulation. Note to Practitioners—In complex modern engineering systems, the consensus control of multi-agent systems has become a research hotspot. In practical industrial applications, given the requirements for safety and production efficiency, it is crucial for systems to effectively constrain states and ensure performance. This study employs the prescribed performance strategy within the fixed-time framework to construct the control method. The constraints of the system’s full states are achieved by transforming a constrained system into an unconstrained one. Meanwhile, the use of event-triggered mechanisms saves communication resources. The proposed method not only ensures rapid consensus convergence under full-state constraints but also enhances the control performance of multi-agent systems, closely connected to the needs of practical applications. Future research will continue to investigate how to apply it to practical engineering applications.

With the initiation of the Internet of Everything, edge computing has emerged as a pivotal paradigm, shifting from cloud computing to better address the growing data demands and latency issues in Industrial Internet of Things (IIoT). However, securing edge computing systems remains a critical challenge as malicious attackers can compromise the IIoT systems, gain control over edge servers, and tamper with computation programs and results. Existing solutions, such as cryptographic encryption, intrusion detection, and blockchain-based methods, have been widely used to enhance security. Yet, these approaches often suffer from high computational overhead, limited adaptability to dynamic IIoT environments, and a lack of foundational trusted assurance mechanisms. Although Trusted Execution Environment (TEE)-based solutions provide a hardware-enhanced secure execution environment, they face scalability and usability challenges and cannot fully support the parallel execution requirements of multiple and diverse IIoT applications. To overcome these limitations, a novel secure edge computing system is proposed for IIoT that strengthens security from the physical layer. By establishing a computing sandbox model, we extend the trust boundaries of the TEE using a virtual Trusted Platform Module (TPM), enabling secure and efficient execution for diverse IIoT applications. The proposed approach integrates a trust guarantee mechanism with decentralized adaptive attestation, ensuring real-time integrity verification while reducing performance overhead. Through security analysis and experimental validation, it is shown that our system improves Non-Volatile Random-Access Memory (NVRAM) launch time by approximately 1,700 times compared to hardware TPM-based virtual TPM implementations, while enhancing protection against attacks such as rollback.