Access Control Technology Research Articles

Innovative IoT-Based Automatic Gate System with RFID and Electro-Magnetic Lock for Secure Access

In this study, we developed an innovative IoT-based automatic gate system aimed at enhancing security and providing flexible access control. The system incorporates an ESP32 microcontroller with integrated Wi-Fi, enabling seamless remote access via mobile devices. It also features RFID technology for reliable physical access control when an internet connection is unavailable. To ensure user safety, an HC SR-04 ultrasonic sensor is implemented to detect obstacles during gate movement, preventing potential accidents. The security of the system is further reinforced by a dual-layer mechanism utilizing an electromagnetic lock (Emlock), which activates upon gate closure to prevent unauthorized access and deactivates when the gate opens. Experimental results indicate that the system effectively addresses the shortcomings of conventional gate control methods, delivering improved security, convenience, and safety for users. Performance tests confirm the reliable operation of both RFID and mobile control functions, with minimal delays observed in sensor response times. This comprehensive solution is well-suited for residential and commercial properties, offering a modern approach to automatic gate security.

Blockchain empowered dynamic access control for secure data sharing in collaborative emergency management

In the realm of emergency management involving multi-party collaboration, securely sharing data among multiple entities poses a significant challenge. Traditional centralized data platforms struggle with fine-grained access control for data from different sources and are susceptible to single-point failures and risks of forged authorization. This paper delves into the prospective application of blockchain technology for decentralized data governance. We propose a dynamic access control technology for multi-party collaborative emergency response, which combines blockchain and attribute-based access control technologies to enable dynamic access control in multi-party data sharing scenarios. A data sharing system for collaborative emergency management built with Hyperledger Fabric has been developed and subjected to a series of evaluation tests. The experimental results reveal that our blockchain-based system not only significantly enhances data security and privacy by leveraging decentralized control but also improves the efficiency and reliability of data sharing in emergency situations. Specifically, our findings demonstrate the system's ability to dynamically adapt access control policies based on changing emergency scenarios, effectively mitigating risks associated with single-point failures and unauthorized access. These outcomes underscore the viability and reliability of our approach, providing a robust framework for secure, scalable, and flexible data sharing in the demanding field of emergency management.

Data Ownership in the AI-Powered Integrative Health Care Landscape.

In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.

Combining Pretty Good Privacy and Role-Based Access Control Technologies for Access Protection to Confidential Data

Combining Pretty Good Privacy and Role-Based Access Control Technologies for Access Protection to Confidential Data

Multidimensional classification matrix for information security risk assessment

In this study, we address one of the key challenges related to a comprehensive risk assessment system for information security concerning personnel during access delineation to company information resources. The relevance of this research is confirmed by numerous instances of information leaks, which highlight the insufficient effectiveness of traditional classification and access control methods. The research aims to analyze existing classification strategies for company information resources and develop an additional method based on continuous access analysis and dynamic adjustment of resource classification. To achieve this goal, we employed methods such as analyzing current information classification strategies, combining various classification techniques, and implementing a graphical method that combines traditional resource classification with a dynamic component using a multidimensional matrix. The main results of the study involve the development of an enhanced method that allows continuous analysis of personnel access to company information resources and dynamic adjustments to resource classification based on access delineation rules. The proposed approach allows for the inclusion of any number of indicators in a graph as a set of vectors, subsequently calculating overall risk assessments based on the sum or difference of these vectors. The practical value of this work lies in its ability to fully utilize modern access control technologies and serve as a foundation for further research, such as automated information classification using neural network training. Additionally, within this study, we conducted a detailed review of existing risk assessment methods for company information resources, identifying key limitations inherent in traditional approaches. Specifically, we analyzed methods based on fixed access levels and the use of static rules for access control. It became evident that such methods are inadequate in responding to dynamic changes in user behavior and the evolving importance of information resources. Thus, the proposed approach allows for more flexible and adaptive access control to information resources, achieved through continuous access monitoring and automatic adjustments based on behavioral user data and contextual changes in resource utilization.

A Novel Method of Secured Data Distribution Using Sharding Zkp and Zero Trust Architecture in Blockchain Multi Cloud Environment

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model.

Studying the Intelligent Power Distribution and Consumption of the Power Grid and its Cost Structure Using Big Data Analysis

Based on the analysis of big data, this paper studies the impact of user behavior response on the cost structure of the microgrid system in the power grid system. The article first conducts in-depth research on distributed power generation and energy storage systems, focusing on the principles and output characteristics of smart power distribution and utilization in power grids, researches smart power distribution and utilization systems in power grids, and makes a more comprehensive discussion of the current situation. Secondly, a big data analysis platform was built, and distributed storage and computing were studied. The platform was used to perform distributed storage and regulation of electricity consumption data, and the electricity consumption information data was divided into the important load, controllable load and transferable load, constructed a microgrid system model based on electricity consumption behavior response, and analyzed a calculation example. After that, a micro-grid system was simulated based on HOMER software, and the optimal capacity configuration of the system was performed. Under this configuration, the micro-grid system has the highest economic efficiency. At the same time, a demand-side load control system was built. Introduced distributed power as a controllable load, integrated new energy access and load control technology, coordinated the contradiction between the power grid and distributed power, and completed a cost-benefit analysis. Finally, for demand-side management electricity price response, peak-valley time-of-use price, the most important implementation method, according to the cost structure theory, the peak-valley period is divided according to the membership function in fuzzy mathematics, and the user's response model to peak-valley time-of-use price is established. The experiment uses the original data to simulate, find the user response model parameters based on the load transfer rate, and complete the comparative analysis of the effect under the peak-valley time-of-use electricity price, which is of great significance to the implementation and improvement of the peak-valley time-of-use electricity price project. The analysis results of the calculation examples show that the method constructed in this paper can effectively realize the power quality analysis of the distribution network in the big data environment. The research results provide technical support for the management of the rural grid voltage deviation of the power company, and lay the foundation for improving the safe operation and management of the power grid.

Analysis and Research on Secure Access Control Technology of Industrial Internet of Things Based on ZTM Model

The security threat of IIoT is becoming increasingly serious. In order to address this challenge, security access control technology based on the ZTM model has become a hot research topic. The aim of this study is to conduct in-depth analysis and research on the security access control technology applied by the ZTM model in industrial Internet of Things environments. By analyzing the current challenges of IIOT security and the limitations of traditional security models, this paper proposes a series of security access control technologies related to the ZTM model, aiming to quantify and evaluate the effectiveness of access control policies, zero trust of the system, and comprehensive risk assessment. By using empirical research methods, this study verified the feasibility of the proposed technology in actual industrial Internet of Things environments and demonstrated the significant effect of the ZTM model in reducing security risks and improving system credibility. The experimental results showed that the optimized security access control technology improved security performance by 28% and the missed detection rate was as low as 3.2%. This study provides useful insights for practical applications in the field of secure access control and provides a solid foundation for future research.

Low-voltage and high-penetration distributed photovoltaic access control technology based on intelligent measurement

Abstract This project takes high-penetration distributed photovoltaic access as the background and studies the control method for low voltage and high penetration distributed photovoltaic access. Firstly, by analyzing the measured data of the low-voltage distribution network, the daily load and measured values of the low-voltage distribution network were obtained. On this basis, the topology structure of a high-penetration distributed photovoltaic grid-connected system is studied, and the voltage, current, power, and other parameters of the grid-connected system are obtained. A low-voltage and high-penetration distributed photovoltaic grid-connected control method based on a genetic algorithm is adopted. Experiments have shown that the maximum allowable capacity of this scheme is smaller than the standard value and can effectively suppress voltage fluctuations. The experimental results show that the system has good grid connection performance, which is of great significance for improving the operational stability and efficiency of low-voltage distribution systems.

ИННОВАЦИОННЫЕ ТЕХНОЛОГИИ АУТЕНТИФИКАЦИИ И УПРАВЛЕНИЯ ДОСТУПОМ

The scientific article explores innovative authentication and access control technologies in the modern information society. The article analyzes the existing authentication and access control methods, as well as examines the latest trends and developments in this area. Special attention is paid to the use of biometric data, machine learning and artificial intelligence to ensure the safety and convenience of users. The article also discusses the advantages, challenges and prospects of using innovative authentication and access control technologies in various fields, including information security, financial services, healthcare and others.

Zero Trust-Based Blockchain Based IoT Security with Consensus and Access Control Framework

As the Internet and computer technology develop, more gadgets are linked wirelessly, expanding the Internet of Things (IoT). IoT is a huge network of sensors and gateways that links them. IoT devices generate images, music, video, digital signals, and more by interacting with their surroundings. To exchange resources and information, all IoT equipment and apps may connect to the Internet. Everything is connected in our world. Due to the broad deployment and massive size of IoT devices, access control of device resources is problematic. Obtaining IoT device resources unlawfully will have major implications since they include personal and sensitive information. Many systems and situations employ access control technologies to secure resources. Discriminatory, identity-based, and MAC access control schemes are traditional (mandatory access control). However, these centralized methods have single-point failure, scalability issues, poor dependability, and low throughput. IoT devices may belong to several organizations or people, be mobile, and function badly, making centralized access management problematic. Another innovative data management solution is blockchain, which uses distributed storage to stabilize data. A transaction writes the data reading or modification record into a block, and the blocks are connected as a chain using a hash to maintain data integrity. It synchronizes data between nodes via a peer-to-peer network and consensus process, assuring data consistency for blockchain network participants. Zero Trust-Based Blockchain, an open source blockchain development platform, offers more efficient consensus methods, larger throughputs, smart contracts, and support for different organizations and ledgers. Proposed work build the fabric-IoT access control system using Zero Trust-Based Blockchain to apply blockchain technology to IoT access control in this study. Distributed processing and storage for IoT data may solve these critical issues with blockchain. Thus, developing distributed IoT-based e-healthcare services using blockchain technology may have been feasible. FabricIoT can keep records, handle dynamic access control, and solve the IoT access control problem using distributed architecture.

An electronic medical record access control model based on intuitionistic fuzzy trust

Electronic Medical Records (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and use, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet in the dynamic and complex healthcare environment. We create a model to solve the privacy leakage problem of healthcare big data. The model utilizes the Latent Delicacy Allocation (LDA) topic model to quickly search documents and extract the topic probability distributions between the target patient and other patients' electronic medical records, which improves the efficiency of medical record document analysis; Calculating physician access similarity, we quantify physician access behavior to more accurately describe physician access behavior in the form of data. We combined the intuitionistic fuzzy theory with the trust evaluation method to create a novel trust evaluation method-- Intuitionistic Fuzzy Trust Evaluation Method, which can reflect the uncertainty characteristics that exist in the physician's diagnostic and treatment process when quantifying the trust of the access by using this method, and it is more flexible and practical, which enhances the accuracy of the quantification of the trust. Improving the traditional trust aggregation method by adding a time window and time decay function, the optimized method improves the accuracy of trust aggregation, and the experimental results are more in line with the actual law. We created a new intuitionistic fuzzy trust access control model using a relative entropy-based intuitionistic fuzzy clustering algorithm and a reward and punishment mechanism to achieve adaptive and dynamic access control for physician access behavior. Experiments show that the access control model based on intuitionistic fuzzy trust proposed in this article achieves 95% correctness in identifying user access behaviors and 94.29% correctness in identifying excessive user access behaviors. Experiments have proved that this model has obvious advantages over other models in the process of privacy protection of electronic medical record systems and has certain control effects.

Mitigating Risks in the Cloud-Based Metaverse Access Control Strategies and Techniques

The advent of the metaverse has revolutionized virtual interactions and navigation, introducing intricate access control challenges. This paper addresses the need for effective access control models in the cloud-based metaverse. It explores its distinct characteristics, including its dynamic nature, diverse user base, and shared spaces, highlighting privacy concerns and legal implications. The paper analyzes access control principles specific to the cloud-based metaverse, emphasizing least privilege, separation of duties, RBAC, defense-in-depth, and auditability/accountability. It delves into identity verification and authorization methods, such as biometrics, multi-factor authentication, and role-based/attribute-based authorization. Advanced access control technologies for the cloud-based metaverse are examined, including SSO solutions, blockchain-based access control, ABAC, adaptive access control, and VMI for isolation. Risk mitigation strategies encompass IDS/IPS, SIEM, and user education programs.

Enhanced Cauchy Matrix Reed-Solomon Codes and Role-Based Cryptographic Data Access for Data Recovery and Security in Cloud Environment

In computer systems ensuring proper authorization is a significant challenge, particularly with the rise of open systems and dispersed platforms like the cloud. Role-Based Access Control (RBAC) has been widely adopted in cloud server applications due to its popularity and versatility. When granting authorization access to data stored in the cloud for collecting evidence against offenders, computer forensic investigations play a crucial role. As cloud service providers may not always be reliable, data confidentiality should be ensured within the system. Additionally, a proper revocation procedure is essential for managing users whose credentials have expired. With the increasing scale and distribution of storage systems, component failures have become more common, making fault tolerance a critical concern. In response to this, a secure data-sharing system has been developed, enabling secure key distribution and data sharing for dynamic groups using role-based access control and AES encryption technology. Data recovery involves storing duplicate data to withstand a certain level of data loss. To secure data across distributed systems, the erasure code method is employed. Erasure coding techniques, such as Reed-Solomon codes, have the potential to significantly reduce data storage costs while maintaining resilience against disk failures. In light of this, there is a growing interest from academia and the corporate world in developing innovative coding techniques for cloud storage systems. The research goal is to create a new coding scheme that enhances the efficiency of Reed-Solomon coding using the sophisticated Cauchy matrix to achieve fault tolerance

Improved Cauchy Reed-Solomon Codes for Cloud Data Retrieval and Secured Data Storage using Role-Based Cryptographic Access and forensic investigation

Doling out client consent strategies to PC frameworks presents a huge test in guaranteeing legitimate approval, especially with the development of open frameworks and scattered stages like the cloud. RBAC has turned into a broadly involved strategy in cloud server applications because of its versatility. Granting access to cloud-stored data for investigating potential wrongdoings is crucial in computer forensic investigations. In cases where the cloud service provider's reliability is questionable, maintaining data confidentiality and establishing an efficient procedure for revoking access upon credential expiration is essential. As storage systems expand across vast networks, frequent component failures require stronger fault tolerance measures. Our work secure data-sharing system combines role (Authorized) based access control and AES encryption technology to provide safe key distribution and data sharing for dynamic groups. Data recovery entails protecting data dispersed over distributed systems by storing duplicate data and applying the erasure code technique. Erasure coding strategies, like Reed-Solomon codes, guarantee disc failure robustness while cutting down on data storage expenses dramatically. They do, however, also result in longer access times and more expensive repairs. Consequently, there has been a great deal of interest in academic and business circles for the investigation of novel coding strategies for cloud storage systems. The objective of this study is to present a novel coding method that utilizes the intricate Cauchy matrix in order to improve Reed-Solomon coding efficiency and strengthen fault tolerance.

PPFLHE: A privacy-preserving federated learning scheme with homomorphic encryption for healthcare data

Healthcare data are characterized by explosive growth and value, which is the private data of patients, and its characteristics and storage environment have brought significant issues of data privacy and security. People are reluctant to share their data for privacy concerns during machine learning. To balance this contradiction, Federated Learning was proposed as a solution to train on private data without sharing it. However, many studies show that there is still the possibility of privacy leakage during the training process of federated learning. In light of this, we propose a privacy-preserving federated learning scheme with homomorphic encryption(PPFLHE). Specifically, on the client side, homomorphic encryption technology is used to encrypt the training model shared by users to ensure its security and privacy. In addition, to prevent internal attacks, Access Control (AC) technology is used to confirm the user’s identity and judge whether it is trusted; on the server side, the Acknowledgment (ACK) mechanism is designed to remove the dropped or unresponsive users temporarily, which reduces the waiting delay and communication overhead, and solves the problem of user’s exiting during training. Theoretical analysis and experimental results show that the proposed scheme achieves high data utility and classification accuracy (81.53%), and low communication delay while achieving privacy preserving, compared to state-of-the-art methods.

Computer network information security and protection strategies in the era of big data

The Internet has entered the big data era, and as more and more businesses employ big data, cloud computing, the Internet of Things, artificial intelligence, and other technologies, the amount of data on the Internet as a whole has also grown dramatically. While Big Data technology has improved the efficiency of many companies, it has also brought challenges for computer network information security protection technology, including massive data privacy leaks that seriously jeopardize the privacy of Internet users. This paper synthesizes literature review findings proving that computer network information security in the big data era has the characteristics of universality, suddenness, concealment, and intelligence in order to address this issue. It also studies the main network information security technologies, such as network situational awareness, vulnerability scanning, intrusion detection and firewall, and identity authentication. In addition, the paper finds that the current problems of network information security mainly include phishing attacks, computer viruses, ransomware, denial of service attacks, APT attacks and threats from inside the network. Finally, the paper explores a number of protective measures to address existing security issues, including the development of network monitoring and threat intelligence systems, the implementation of access control and authentication technologies, network isolation methods, regular software scanning and updates, and organising internal staff security training. Through this study we are able to fully understand the overall pulse of current computer network information security technology and development trends in the era of Big Data, thus providing a more comprehensive and accurate reference for Big Data-related participants to develop responsive information security protection measures and strategies to provide designation.

Research on Industrial IoT Security Based on Deep Learning

<p>With the rapid development of "Internet +" and the construction of a new generation of information infrastructure, the intrusion behaviors against the Industrial Internet of Things are increasingly common. How to ensure the security of the industrial Internet of Things is one of the current research hotspots. The modern technology trend has the hottest technologies of the Internet of Things (IoT). The application of IoT on the other hand improves work efficiency and brings convenience to people’s life; on the other hand, it makes the network face increasingly serious security threat problems and attacks the network by unscrupulous elements occur from time to time. Machine learning-based intrusion detection techniques involve a large number of mathematical formula operations, while with the development of neural networks, the excellent autonomous feature learning capability of deep learning is recognized. An intrusion detection system plays an important role in preventing security threats and protecting them from attacks. The current research on industrial IoT security technology focuses on authentication technology, encryption technology, access control technology, and intrusion detection technology. In this paper, we analyze deep learning and industrial IoT intrusion detection and use the powerful data processing capability and feature learning capability of deep learning to conduct an in-depth study on industrial IoT intrusion detection methods based on deep learning. This paper achieves a 96.32% detection rate on industrial control dataset, which can better adapt to the needs of industrial IoT intrusion detection.</p> <p> </p>

A Multi-Blockchain-Based Cross-Domain Authentication and Authorization Scheme for Energy Internet

The expansion of the scale of the Power Internet of Things stimulated by the development of the Energy Internet makes the growth in demand for the effective authentication and access control technologies in the cross-domain data exchange. Based on the cross-chain technology of the blockchain and the cuckoo filter, this paper proposes a cross-domain authentication scheme for Power Internet of Things. Firstly, a cross-chain authentication architecture is established. Combined with the existing authentication technologies used in intra-domain authentication, a cross-domain authentication process based on the cross-chain technology is proposed to realize the automatic transmission of the authentication credentials from application domain to authentication domain. The cuckoo filter is deployed on the blockchain as smart contracts, and the user certificate fingerprint is inserted into the filter to realize user registration, query, and revocation, which reduces the cost of the user certificate management. Experimental results show the effectiveness and feasibility of our scheme. Based on the proposed authentication scheme, a cross-domain access control scheme based on roles and object classes is presented, by treating the object classes as controlled objects and then applying the role-based access control to the object classes, on the condition that the heterogeneous domains in the Energy Internet have the same kinds of resources.

Image Measurement Attribute Extraction Method under Large Error Condition

In recent years, the rapid development of image processing based on deep learning has had a wide impact on industrial production and social life. In the field of information security, image content description as a kind of attribute has been used in authentication, access control and other technologies. In addition to describing what is in an image, fine-grained image content properties should also be able to express the relative size and position relationships between objects. Due to the strong fuzziness of the relative scale between objects in the image, it is not suitable for precise definition. This paper proposes a method to extract measurement attributes under large error to describe the relative size of two parts in an image. Based on image classification and object detection, the error range is estimated by statistical method, and a fuzzy set is constructed as the length unit. Then the contradictory critical points of measurement are used to determine the comparable scale range and similar regions. Finally, the measurement attribute of image content is given. Experiments show that the size description obtained by this method accords with people’s subjective feelings, and can effectively extract the fine-grained attributes of the image.