The development of the Internet of Things (IoT) and 5th generation wireless network (5G) is set to push the smart agriculture to the next level since the massive and real-time data can be collected to monitor the status of crops and livestock, logistics management, and other important information. Recently, COVID-19 has attracted more human attention to food safety, which also has a positive impact on smart agriculture market share. However, the security and privacy concern for smart agriculture has become more prominent. Since smart agriculture implies working with large sets of data, which usually sensitive, some are even confidential, and once leakage it can expose user privacy. Meanwhile, considering the data publishing of smart agriculture helps the public or investors to real-timely anticipate risks and benefits, these data are also a public resource. To balance the data publishing and data privacy, in this paper, a privacy-preserving data aggregation scheme with a flexibility property uses ElGamal Cryptosystem is proposed. It is proved to be secure, private, and flexible with the analysis and performance simulation.

The integration of Internet of Things (IoT) devices in healthcare has enhanced medical efficiency but poses challenges such as data privacy risks and internal abuse. Traditional IoT data access frameworks suffer from centralization, limited scalability, and static permission controls. To address these issues, we propose MedAccessX, a blockchain-based access control framework combining attribute-based access control (ABAC) and role-based access control (RBAC). MedAccessX utilizes four types of smart contracts: a user management contract (UMC) for managing user operations, a medical data management contract (MDMC) for handling data, a policy contract (PC) for managing access rights, and an access control contract (ACC) for enforcing permissions and facilitating data sharing. Our evaluation, conducted on a private Ethereum blockchain network with multiple nodes, assesses security, deployment cost, gas consumption, throughput, and response time. Comparative analysis demonstrates that MedAccessX achieves lower deployment costs and higher throughput, outperforming existing solutions.

The rapid expansion of the Internet of Things (IoT) has made software security and reliability a critical concern. With multi-language programs running on edge computing, embedded systems, and sensors, each connected device represents a potential attack vector, threatening data integrity and privacy. Symbolic execution is a key technique for automated vulnerability detection. However, unknown function interfaces, such as sensor interactions, limit traditional concrete or concolic execution due to uncertain function returns and missing symbolic expressions. Compared with system simulation, the traditional method is to construct an interface abstraction layer for the symbolic execution engine to reduce the cost of simulation. Nevertheless, the disadvantage of this solution is that the manual modeling of these functions is very inefficient and requires professional developers to spend hundreds of hours. In order to improve efficiency, we propose an LLM-based automated approach for modeling unknown functions. By fine-tuning a 20-billion-parameter language model, it automatically generates function models based on annotations and function names. Our method improves symbolic execution efficiency, reducing reliance on manual modeling, which is a limitation of existing frameworks like KLEE. Experimental results primarily focus on comparing the usability, accuracy, and efficiency of LLM-generated models with human-written ones. Our approach was integrated into one verification platform project and applied to the verification of smart contracts with distributed edge computing characteristics. The application of this method directly reduces the manual modeling effort from a month to just a few minutes. This provides a foundational validation of our method’s feasibility, particularly in reducing modeling time while maintaining quality. This work is the first to integrate LLMs into formal verification, offering a scalable and automated verification solution for sensor-driven software, blockchain smart contracts, and WebAssembly systems, expanding the scope of secure IoT development.

Recently, deep learning has made significant inroads into the Internet of Things due to its great potential for processing big data. Backdoor attacks, which try to influence model prediction on specific inputs, have become a serious threat to deep neural network models. However, because the poisoned data used to plant a backdoor into the victim model typically follows a fixed specific pattern, most existing backdoor attacks can be readily prevented by common defense. In this paper, we leverage natural behavior and present a stealthy backdoor attack for image classification tasks: the raindrop backdoor attack (RDBA). We use raindrops as the backdoor trigger, and they are naturally merged with clean instances to synthesize poisoned data that are close to their natural counterparts in the rain. The raindrops dispersed over images are more diversified than the triggers in the literature, which are fixed, confined, and unpleasant patterns to the host content, making the triggers more stealthy. Extensive experiments on ImageNet and GTSRB datasets demonstrate the fidelity, effectiveness, stealthiness, and sustainability of RDBA in attacking models with current popular defense mechanisms.