Privacy protection of surveillance videos is an important issue because of the pervasiveness of surveillance cameras. Region of interest (RoI) privacy protection prefers encrypting limited privacy-sensitive areas to encrypting the entire video because of the resource-tightened Internet of Things devices. However, many cryptographic technologies now applied in privacy protection, e.g., AES and RSA, are not efficient to meet the real-time requirement for surveillance videos. A lightweight encryption approach is then devised based on layered cellular automata (LCA). Using our approach, the extracted RoIs are fed into the initial state of an 8-layer cellular automata, which is driven by the randomly selected rules for the LCA's state transitions. As a result, all RoIs are encrypted independently and synchronously. The encrypted RoIs are stored at the camera side and can be used by authenticated users through an on-demand manner. The surveillance video without RoIs can be watched in real-time by any user online. Theoretical analyses and experimental results show that our approach is both efficient and effective.

The study of lightweight block ciphers has been a “hot topic”. As one of the main structures of block ciphers, the Feistel structure has attracted much attention. However, the traditional Feistel structure cipher changes only half of the plaintext in an iterative round, resulting in slow diffusion. Therefore, more encryption rounds are required to ensure security. To address this issue, a new algorithm, GFRX, is proposed, which combines a generalized Feistel structure and ARX (Addition or AND, Rotation, XOR). The GFRX algorithm uses an ARX structure with different non-linear components to deal with all the branches of a generalized Feistel structure so that it can achieve a better diffusion effect in fewer rounds. The results of a security analysis of the GFRX algorithm show that the effective differential attacks do not exceed 19 rounds and that the effective linear attacks do not exceed 13 rounds. Therefore, the GFRX algorithm has an adequate security level for differential and linear analysis. Avalanche test results obtained for the GFRX algorithm show that the GFRX algorithm has strong diffusion and only takes six rounds to meet the avalanche effect. In addition, the GFRX algorithm can achieve different serialization levels depending on different hardware resource requirements and can achieve full serialization, which ensures operational flexibility in resource-constrained environments.

Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods that can securely aggregate models have received widespread attention. Federated learning enables clients to train models locally and share their model updates with the server. While this approach allows collaborative model training without exposing raw data, it still risks leaking sensitive information. To enhance privacy protection in federated learning, secure aggregation is considered a key enabling technology that requires further in-depth investigation. This paper summarizes the definition, classification, and applications of federated learning; reviews secure aggregation protocols proposed to address privacy and security issues in federated learning; extensively analyzes the selected protocols; and concludes by highlighting the significant challenges and future research directions in applying secure aggregation in federated learning. The purpose of this paper is to review and analyze prior research, evaluate the advantages and disadvantages of various secure aggregation schemes, and propose potential future research directions. This work aims to serve as a valuable reference for researchers studying secure aggregation in federated learning.

The temperature and humidity control of a pig house is a complex multivariable control problem. How to keep the temperature and humidity in a pig house within a normal range is the problem to be solved in this paper. The traditional threshold-based environmental control system cannot meet this requirement. In this paper, an intelligent control system of temperature and humidity in a pig house based on machine learning and a fuzzy control algorithm is proposed. We use sensors to collect the temperature and humidity in the pig house and store these data in chronological order. Then, we use these time series data to train the GRU model and then use the GRU model to predict the temperature and humidity change curve in the pig house in the next 24 hours. Finally, the mathematical model of the pig house and related equipment is established, and the output power of the related equipment is calculated based on the prediction results of GRU so as to effectively regulate the indoor temperature and humidity. The experimental results show that compared with the threshold-based environmental control system, our system reduces the abnormal temperature and humidity by about 90%.