SEPP-FLBC: A Secure and Efficient Privacy Protection Scheme Using Federate Learning and Blockchain for Edge-End-Cloud Devices

Industrial Internet of Things (IIoT) is a fusion of industrial automation systems and IoT systems. It features comprehensive sensing, interconnected transmission, intelligent processing, self-organization and self-maintenance. Its applications span intelligent transportation, smart factories, and intelligence. Many areas such as power grid and intelligent environment detection. With the widespread application of IIoT technology, the cyber security threats to industrial IoT systems are increasing day by day, and information security issues have become a major challenge in the development process. In order to protect the industrial IoT system from network attacks, this paper aims to study the industrial IoT information security protection technology, and the typical architecture of industrial Internet of things system, and analyzes the network security threats faced by industrial Internet of things system according to the different levels of the architecture, and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.

At present, industrial Internet data is facing a severe security situation in storage, transmission, and use. This paper proposes data security management and security protection strategies by studying the data security risks and status quo of industrial Internet platforms. Among them, the security management strategy includes data classification and classification, data security assessment and data security threat emergency management. The security protection strategy deploys industrial data security protection from the technical aspects of collection, storage, transmission, processing, sharing, and deletion, providing a reference for further strengthening the industrial Internet data security.

Database security protection, database backup and disaster recovery are important tasks for all colleges and universities to ensure the safe and stable operation of information systems. Based on the operating environment of the Oracle production database in China University of Geosciences (Beijing), combined with the practical operation and maintenance experience, this paper provides a design and implementation case of Oracle database security protection system and disaster recovery architecture. The network security protection architecture of the three-layer firewall and fortress machine, the detection and repair of security vulnerabilities, the management of system accounts and permissions, data encryption and database audit constitute the security protection system of the database. Oracle RAC (Real Application Clusters), Oracle DataGuard, redundant backup management and backup recovery constitute the disaster recovery architecture of the database. The case has practical significance for database operation and maintenance management in other colleges and universities.

During the last few years there has been an increasing number of people-centric sensing projects. These combine location information with sensors available on mobile phones, giving birth to a different dimension in sensing our environment and providing us with new opportunities to create collective intelligence systems to address urban-scale problems such as air pollution, noise, and traffic. However, as people are directly involved in the collection process, they often inadvertently reveal information about themselves, raising new and important privacy concerns. While standard privacy enhancing technologies exist, they do not fully cover the many peculiarities of these new pervasive applications. The ubiquitous nature of the communication and the storage of location traces compose a complex set of threats on privacy, which we overview in this article. Then we go through the latest advances in security and privacy protection strategies, and discuss how they fit with this new paradigm of people-centric sensing applications. We hope this work will better highlight the need for privacy in urban sensing applications and spawn further research in this area.

The growing use of cloud services, increased number of users, novel mobile operating systems and changes in network infrastructures that connect devices create novel challenges for cyber security. In order to counter arising threats, network security mechanisms and protection schemes also evolve and use sophisticated sensors and methods. The drawback is that the more sensors (probes) are applied and the more information they acquire, the volume of data to process grows significantly. In this paper, we present real-time network data analysis mechanism. We also show the results for SQL Injection Attacks detection.

An image encryption scheme based on an improved Hilbert curve and a novel 4D hyper-chaotic system

The development of manufacturing, communication, and computing technologies has promoted convergence of different network architectures, aiming to make full use of all resources to create valuable information. While heterogeneous integrated networks play a key role in providing highly effective and efficient solutions for medical users, they also raise various security and privacy issues. Strengthen the security and privacy protection and efficiency before transmitting sensitive data is a primary challenge to be addressed. Traditional authentication methods fail to differentiate the privacy sensitivity difference between data collected by distinct devices. We propose a secure and efficient key management scheme based on a key-exposure resistant chameleon hash function. Through adopting the devices' privacy sensitivity and users' attribute credential as two factors to establish trust between different entities, we proposed a secure and anonymous authentication protocol. The security analyses indicate that the proposed protocol can satisfy security requirements.

This article is based on blockchain and Internet of Things (IoT) technology, combined with the needs of electronic archive management, aiming to address privacy and security issues in the process of data sharing. At present, there are still performance bottlenecks in blockchain networks, the impact of data noise on model accuracy in federated learning, and insufficient privacy protection in data sharing. In response to these shortcomings, this article proposes an electronic waste management method that combines blockchain, federated learning, and differential privacy. It automatically executes the data sharing process through smart contracts, uses InterPlanetary File System (IPFS) to achieve distributed storage of data, and designs an incentive mechanism based on Shapley values to ensure fair returns for data owners during the sharing process. The main contribution of this article is to improve the efficiency and security of data sharing, enhance the privacy protection of the model, and optimize the collaboration mechanism between nodes. The experimental results show that this method has significant advantages in improving data sharing efficiency and the accuracy of the highest data quality s_9 achieves 98.2%, enhancing privacy protection, and system security, while ensuring the accuracy of the model and the scalability of the system.

An efficient privacy protection scheme for data security in video surveillance

In recent years, remote data management environments have been increasingly deployed across diverse infrastructures, accompanied by a rapid surge in the demand for sharing and collaborative processing of sensitive data. Consequently, ensuring data security and privacy protection remains a fundamental challenge. A representative example of such an environment is the cloud, where efficient mechanisms for secure data sharing and access control are essential. In domains such as finance, healthcare, and public administration, where large volumes of sensitive information are processed by multiple participants, traditional access-control techniques often fail to satisfy the stringent security requirements. To address these limitations, Key-Aggregate Proxy Re-Encryption (KA-PRE) has emerged as a promising cryptographic primitive that simultaneously provides efficient key management and flexible authorization. However, existing KA-PRE constructions still suffer from several inherent security weaknesses, including aggregate-key leakage, ciphertext insertion and regeneration attacks, metadata exposure, and the lack of participant anonymity within the data-management framework. To overcome these limitations, this study systematically analyzes potential attack models in the KA-PRE setting and introduces a novel KA-PRE scheme designed to mitigate the identified vulnerabilities. Furthermore, through theoretical comparison with existing approaches and an evaluation of computational efficiency, the proposed scheme is shown to enhance security while maintaining practical performance and scalability.

In the era of digital intelligence, data is a key element in promoting social and economic development. Educational data, as a vital component of data, not only supports teaching and learning but also contains much sensitive information. How to effectively categorize and protect sensitive data has become an urgent issue in educational data security. This paper systematically researches and constructs a multi-dimensional classification framework for sensitive educational data, and discusses its security protection strategy from the aspects of identification and desensitization, aiming to provide new ideas for the security management of sensitive educational data and to help the construction of an educational data security ecosystem in the era of digital intelligence.

With the continuous development and popularisation of Internet of Things (IoT) technology, the regulation and protection of computer network information security has become increasingly important. The application of IoT information technology enables various devices and systems to communicate and interact with each other, however, it also brings new security challenges and risks. In this context, it is particularly urgent and necessary to develop effective regulation and protection strategies. This paper focuses on computer network information security in the Internet of Things environment, considering the rapid development of the Internet of Things and its unique security challenges, to study the regulation and protection strategies to adapt to this field. First, this content investigates the shortcomings of traditional information security strategies in dealing with the diversity, large-scale deployment, data privacy protection, real-time monitoring and cross-border integration of the IOT, and discusses the important exploration results and existing defects in the field of IOT security in the current academia and industry. Based on a step-by-step exploration of existing references and methods, this content designs a multi-level security protection system that covers security requirements from the device layer to the application layer. In threat detection, the system shows an average of 95% threat detection accuracy, ensuring efficient security monitoring. The system stability and performance evaluation experiment shows that the CPU usage maintenance rate is about 65% on average, and the memory usage is about 55% on average. The above data indicates that the system maintains good stability while operating efficiently. In the adaptive defense mechanism effect test, the system shows an average of about 3 defense mechanism adjustments per day, indicating its high adaptability in a dynamic security environment. The above experimental results jointly verify the effectiveness and reliability of the proposed security strategy in the IOT environment.

Development and improvement has brought a very great impact to the network technology of the computer, besides the security of the network information has become a focus of the social safety questions.In this paper, through the analysis of security threats to computer network information and protective factors conducted, a strategy for a common computer network information security and protection strategy has been proposed, and useful lessons for our computer network and information security and protection reference have been provided.A series of questions such as security and dependability regarding the computer network system arise, which has been taken into consideration in proposing the strategy.This paper puts forward proposals with respects to the importance of the computer network system, existing problems of the online security of the computer, and precautionary measures.The detailed discussion will help the mass users to become conscious regarding network security and take precautionary measures while using the network.

With the rapid development of information technology and the advancement of smart city construction, urban management and services increasingly rely on networked intelligent systems. However, this highly interconnected network environment has also brought numerous network security challenges to smart cities. Smart cities involve a large number of Internet of Things (IoT) devices, big data platforms, cloud computing technologies, and smart infrastructure, making network security problems more complex and severe. This paper analyzes the composition and development trends of smart cities, explores the main security risks currently faced, and focuses on the security risks in key areas such as the Internet of Things, cloud computing, big data, and intelligent transportation systems. Furthermore, this paper proposes a multi-layered protection system and strategies for smart city network security, aiming to improve the network security assurance level of smart cities. The research seeks to provide practical protection measures to help smart cities cope with the growing security threats and ensure their sustainable development.

To study and solve the issues of big data security and privacy protection is the key to promote the healthy development of big data. In this paper, a 3D big data security and privacy protection model based on life cycle, security goal and user role is presented, and the security challenges and security goals of big data are analyzed from different aspects. The feasible technology and its application in big data security and privacy protection are expounded and analyzed, and the in-depth research direction of big data security and privacy protection is discussed and summarized.