This paper presents an agricultural management service system that aims to meet the needs of Internet of Things (IoT) information upgrades in China’s family farms. The proposed agricultural management service system consists of Near Field Communication (NFC) tags, in-field service nodes, and smartphones. NFC tags are used as the core identifier of various agricultural management elements. The in-field service node, which is based on a programmable system-on-chip with intellectual property cores (IP core), supports distributed agriculture device management and smartphone operations. Smartphones in the proposed system include the management assistant application (app) and management service app, which are designed for agricultural management support functions and agricultural management application requirements. Through this system, the needs of diverse agricultural management practices can be effectively satisfied by a unified system structure. The practical results show that the design can be used to construct diversified agricultural IoT information application service systems simply and effectively, and it is especially suitable for Chinese family farm operators who are implementing IoT information upgrades for smart agriculture.

As a privacy-preserving enhancement to the federated learning (FL) framework, secure aggregation (SA) enables multiparty summation without any party needing to reveal their updates to the aggregator in the Internet of Things applications. However, the conventional threat model underestimates the potential inversion attacks on aggregated gradients from an honest-but-curious client, due to the considering information loss caused by SA. This study for the first time, demonstrates the gradient inversion attack against the SA schemes in which gradients are quantized and aggregated. Then, an enhanced gradient inversion from the client side is proposed to address two roadblocks caused by SA, i.e., aggregation information loss and quantization rounding error. To countermeasure the information loss, we utilize class-wise representation matching to achieve the category-level decomposition. This relies on a prior restoration of the class-wise representations and instance-wise labels, whose numerical accuracy is cyclically calibrated through the prior-based offset estimation. Since, cryptographic operators involved in the SA schemes usually operates in the integer domain, gradient quantization is introduced. Regarding the rounding errors from the gradient quantization, quantization-aware gradient matching (QGM) is presented to align with a more precise optimization objective. Extensive experiments demonstrate that a semi-honest client is sufficient to infer sensitive data from the aggregated gradients after even 8-bit quantization. Moreover, a defense scheme based on 1-bit gradient quantization is proposed. The new attack from the client side in SA-based FL urges the community to take necessary defensive measures.

Intravehicular communication relies on controller area network (CAN) protocol to deliver messages and instructions among different electronic control units (ECU). Unfortunately, inherent defects in CAN include the absence of confidentiality and integrity mechanism, enabling adversaries to launch attacks from wired or wireless interfaces. Although various CAN cryptographic protocols have been proposed for entity authentication and secure communication, the redundancy in the key establishment phase weakens their availability in large-scale CAN. In this paper, we propose a scalable security protocol suite for intravehicular networks and reduce the communication costs significantly. A new type of attack, suspension attack, is identified for the existing protocols and mitigated in our protocol by leveraging a global counter scheme. We formally verify the security properties of the proposed protocol suite through the AVISPA tool. The simulation results indicate that the communication and computation efficiency are improved in our protocol.

TEE-shielded secure inference offers an efficient solution to protect valuable edge-deployed models from potential thefts. Nevertheless, existing methods are lack of theoretical security analysis, failing to achieve the optimal security. Furthermore, while feasible for small models, existing methods are excessively heavyweight for Large Language Models (LLM). For LLaMA-7B, they introduce GB-level secure memory requirement and hundredfold inference latency, severely compromising real-time utility. To solve these problems, we first present a Bayesian theory framework of Model Stealing (MS) attacks, which decomposes MS into prior and posterior knowledge leakage. Based on this framework, LLMGuard is proposed, which presents two components: First, Intrinsic Parameters Shielding is designed to shield all private parameters, preventing prior knowledge leakage. This approach significantly decreases the secure memory usage and achieves inference speedup. Second, since OTP is not applicable to LLMs, Random Slices Composition is developed to obfuscate intermediate distributions with no computational overhead, minimizing posterior knowledge leakage efficiently. Experimental results demonstrate that LLMGuard downgrades model to black-box inference with negligible accuracy loss, while delivering \(43\times\) inference speedup on LLaMA compared to fully-shielded methods. The proposed LLMGuard effectively addresses concerns related to intellectual property theft on edge, boosting the secure deployment of LLMs on untrusted devices.

Data has become a critical driver of innovation in artificial intelligence and the evolution of 6G technologies. The explosive growth of data volume accelerates the convergence of cloud and edge computing, while simultaneously posing heightened challenges to data security and privacy. The emerging cloud-edge-device collaborative paradigm enables dynamic and large-scale data circulation across heterogeneous entities, exposing systems to complex threats such as malicious edge nodes and eavesdropping over untrusted communication channels. In response to these issues, we propose EdgeGuard, a secure and decentralized framework for cloud-edge-device data circulation. EdgeGuard is specifically designed for highly dynamic environments and ensures robust data confidentiality, integrity, traceability, and resilience against both malicious external attackers and compromised edge servers. To underpin its cryptographic foundation, we develop two core primitives. Specifically, we introduce an Aggregatable Publicly Verifiable Secret Sharing (APVSS) scheme that enables efficient sharing of field elements while supporting aggregation and public verifiability. Furthermore, we construct AggDKG, a distributed key generation (DKG) protocol. AggDKG achieves public verifiability and bias resistance with an expected total communication cost of ${\mathcal {O}}(\kappa n^{3})$ effectively overcoming the scalability limitations inherent in traditional complaint-based protocols. Collectively, these components form a comprehensive framework that strengthens secure and efficient data circulation in cloud-edge-device systems. Experimental data show that AggDKG delivers clear performance gains: across all tested scales, its total running time is only about 8%–65% of that of the DKG of Gurkan et al., and at $n=256$, it reduces per-node runtime by approximately 27% compared with the DKG of Gennaro et al. These results highlight EdgeGuard's superior scalability, lower latency, and stronger Byzantine resilience for secure large-scale deployments.