Federated learning (FL) is a kind of privacy-aware machine learning, in which the machine learning models are trained on the users side and then the model updates are transmitted to the server for aggregating. As the data owners need not upload their data, FL is a privacy-persevering machine learning model. However, FL is weak as it suffers from a reverse attack, in which an adversary can get users data by analyzing the user uploaded model. Motivated by this, in this paper, based on the secret sharing, we design EPPDA, an efficient privacy-preserving data aggregation mechanism for FL, to resist the reverse attack, which can aggregate users trained models secretly without leaking the users model. Moreover, EPPDA has efficient fault tolerance [1] for the user disconnection. Even if a large number of users are disconnected when the protocol runs, EPPDA will execute normally. Analysis shows that the EPPDA can provide a sum of locally trained models to the server without leaking any single users model. Moreover, adversary can not get any non-public information from the communication channel. Efficiency verification proves that the EPPDA not only protects users privacy but also needs fewer computing and communication resources.

In order to protect the privacy of individual data, meantime guaranteeing the utility of big data, the privacy preserving data aggregation is widely researched, which is a feasible solution since it not only preserves the statistical feature of the original data, but also masks single user’s data. With smart meter owning the capability of connecting to Internet, the aggregation area extends to the virtual area rather than a traditional physical area. However, in a virtual aggregation area, the users’ membership maybe frequently changes, if while executing the aggregation protocol for the traditional area, the overhead is not ignorable. In this paper, the homomorphic encryption and ID-based signature are employed to design a dynamic membership data aggregation (DMDA) scheme, which reduces the complexity on a new user’s joining and an old user’s quitting. In addition, the operation center obtains the sum of the data in the virtual aggregation area, meantime knows nothing about single user’s data. Comparing with traditional privacy-preserving data aggregation scheme, DMDA is more suitable for next-generation smart grid and other Internet of Things environments.

In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a (k,n) threshold privacy-preserving data aggregation scheme named (k,n)-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to n smart meters (SMs) through the threshold secret sharing algorithm. When k or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than k SMs cannot recover the key. Thus, our solution still works functionally even if up to n−k SMs fail; also, it resists attacks from the collusion of less than k SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.

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.