User Permissions Research Articles

Research on Grid Multi-Source Survey Data Sharing Algorithm for Cross-Professional and Cross-Departmental Operations Collaboration

This paper addresses the problem of multi-source survey data sharing in power system engineering by proposing two improved methods: a survey data sharing method combined with differential privacy and a permission change method based on attribute encryption. The survey data sharing method integrated with differential privacy achieves effective cross-professional and cross-departmental data sharing while ensuring data security by introducing multi-discriminator architecture and dynamic noise adjustment. To reduce the computational and communication overhead when user permissions change during survey data sharing, the attribute encryption-based permission change method supports dynamic changes in user permissions. The effectiveness of the proposed methods has been validated through targeted experiments in different scenarios. The work in this thesis provides a new solution for dynamic sharing of survey data in power network engineering. It contributes to the digital transformation of power network projects.

Anonymous data sharing scheme for resource-constrained internet of things environments

With the rapid development of Internet of Things (IoT) technology in industrial, agricultural, medical and other fields, IoT terminal devices face security and privacy challenges when sharing data. Among them, ensuring data confidentiality, achieving dual-side privacy protection, and performing reliable data integrity verification are basic requirements. Especially in resource-constrained environments, limitations in the storage, computing, and communication capabilities of devices increase the difficulty of implementing these security safeguards. To address this problem, this paper proposes a resource-constrained anonymous data-sharing scheme (ADS-RC) for the IoT. In ADS-RC, we use elliptic curve operations to replace computation-intensive bilinear pairing operations, thereby reducing the computational and communication burden on end devices. We combine an anonymous verifiable algorithm and an attribute encryption algorithm to ensure double anonymity and data confidentiality during the data-sharing process. To deal with potential dishonest behavior, this solution supports the revocation of malicious user permissions. In addition, we designed a batch data integrity verification algorithm and stored verification evidence on the blockchain to ensure the security and traceability of data during transmission and storage. Through experimental verification, the ADS-RC scheme achieves reasonable efficiency in correctness, security and efficiency, providing a new solution for data sharing in resource-constrained IoT environments.

TRIUMPH OF SECURITY : FORTIFYING DIGITAL FORTRESSES WITH TRIPLE GUARD AUTHENTICATION

Abstract : Network security is crucial in today’s interconnected world, and traditional text-based passwords are insufficient. This paper introduces a Three-Level Security system to enhance protection with a multi-tiered approach: Level-1 Login-based Password, Level-2 Graphical Authentication, and Level-3 Image-Based Password. The system ensures authorized access, defines user permissions, and encrypts data. It includes monitoring, rate limiting, regular updates, and penetration testing, using Python-based tools for vulnerability scanning. Triple Guard encrypts data both at rest and in transit, ensuring confidentiality and integrity. It adaptive to emerging threats and vulnerabilities, integrating Python-based security testing tools for vulnerability scanning and code analysis. By providing developers and administrators with a robust and extensible password security solution . Triple Guard encrypts data both at rest and in transit, preserving its confidentiality and integrity. Index Terms :-. Login Password Authentication, Graphical Authentication, Image Authentication, Python, Chatgpt.

Decentralized Identity Authentication Mechanism: Integrating FIDO and Blockchain for Enhanced Security

FIDO (Fast Identity Online) is a set of network identity standards established by the FIDO Alliance. It employs a framework based on public key cryptography to facilitate multi-factor authentication (MFA) and biometric login, ensuring the robust protection of personal data associated with cloud accounts and ensuring the security of server-to-terminal device protocols during the login process. The FIDO Alliance has established three standards: FIDO Universal Second Factor (FIDO U2F), FIDO Universal Authentication Framework (FIDO UAF), and the Client to Authenticator Protocols (CTAP). The newer CTAP, also known as FIDO2, integrates passwordless login and two-factor authentication. Importantly, FIDO2’s support for major browsers enables users to authenticate their identities via FIDO2 across a broader range of platforms and devices, ushering in the era of passwordless authentication. In the FIDO2 framework, if a user’s device is stolen or compromised, then the private key may be compromised, and the public key stored on the FIDO2 server may be tampered with by attackers attempting to impersonate the user for identity authentication, posing a high risk to information security. Recognizing this, this study aims to propose a solution based on the FIDO2 framework, combined with blockchain technology and access control, called the FIDO2 blockchain architecture, to address existing security vulnerabilities in FIDO2. By leveraging the decentralized nature of the blockchain, the study addresses potential single points of failure in FIDO2 server centralized identity management systems, thereby enhancing system security and availability. Furthermore, the immutability of the blockchain ensures the integrity of public keys once securely stored on the chain, effectively reducing the risk of attackers impersonating user identities. Additionally, the study implements an access control mechanism to manage user permissions effectively, ensuring that only authorized users can access corresponding permissions and preventing unauthorized modifications and abuse. In addition to proposing practical solutions and steps, the study explains and addresses security concerns and conducts performance evaluations. Overall, this study brings higher levels of security and trustworthiness to FIDO2, providing a robust identity authentication solution.

Designing a Block Chain Based Network for the Secure Exchange of Medical Data in Healthcare Systems

ABSTRACT An identity-based cryptosystem called signcryption is investigated in this study in order to efficiently and securely exchange healthcare information inside a data-sharing network, utilizing the idea of bilinear pairings. A distributed tamperproof database protects all health records and is uploaded to it and replicated over a group of nodes that are linked to form a peer-to-peer network. As a result, each health record is treated as an individual event that is time stamped and assigned a cryptographic hash. On a similar note, transaction blocks include all the record events. Every node in the network has a copy of the ledger of hashed records and events. Additional information such as user permission lists are also included in this health care blockchain network, which serve as instructions for the network. To guarantee the data integrity of EHRs, this article provides security for exchanging EHRs across medical institutions and inside the organization, and ultimately safe exchange of EHRs by removing a third-party trustworthy third-party provider. Various cryptographic methods are being utilized on the blockchain in order to ensure security for the exchange of health care data. Signcryption is utilized in the suggested design approach for secure health care data exchange.

Virtual Reality and Emotional Responses: A Comprehensive Literature Review on Theories, Frameworks, and Research Gaps

This extensive literature review examines virtual reality (VR) and emotional responses, including theories, paradigms, and research gaps. Foundational to VR is its ability to evoke profound emotional involvement through heightened presence. The study synthesizes presence theory, appraisal theory, and transportation theory to explain how they affect virtual emotional experiences. It also examines affective computing and multisensory integration frameworks for emotional design for VR and their importance in creating emotionally engaging VR experiences. Furthermore, the study highlights key research gaps and issues in the field. These include individual variances in emotional responses, the undiscovered long-term effects of repeated VR exposure, and ethical issues surrounding emotion manipulation and user permission. To fill these gaps, computer science, psychology, and allied sciences should work together to comprehend the complex relationship between VR and human emotions. The ethical, theoretical, and practical aspects of VR’s emotional landscape are explored in this synthesis to guide future research.

Fuzzy Logic-Based Energy Storage Management for Grid Resilience

In this study, we present and examine the implementation of a fuzzy logic-driven energy storage management system devised to enhance the efficiency of charging and discharging activities in modern power grids. By using real data derived from the installation of the system, our research aims to assess its effectiveness in bolstering grid resilience and stability. The system integrates linguistic factors and employs rule-based decision-making, enabling it to adapt charging and discharging techniques in real-time to the prevailing grid circumstances. The results unequivocally indicate a noteworthy enhancement in the efficiency of the system, exhibiting a percentage alteration spanning from 8% to 12%, hence confirming the system’s inherent ability to mitigate energy losses throughout its operating cycles. Moreover, the use of the fuzzy logic controller significantly enhances the Grid Reliability Index, exhibiting a percentage variation ranging from 10% to 15%, so demonstrating a higher level of stability and responsiveness to grid demand. The controller’s flexibility is highlighted by doing sensitivity analysis, which demonstrates its strong decision-making skills across many contexts. The charging and discharging techniques are meticulously tuned, demonstrating a percentage fluctuation of 15% to 20% in accordance with diverse grid situations, effectively optimizing the exploitation of renewable energy sources. The practical usefulness of the proposed system is confirmed by the validation of simulation results against historical data. The research incorporates ethical issues, such as clear communication, user permission, and socio-economic implications, to underscore the appropriate application of modern energy management technology. The results of this study significantly enhance the overall comprehension of fuzzy logic-driven systems, presenting a very promising resolution for the enhancement of energy storage operations and the progression of robust and environmentallyfriendly energy infrastructures.

K-pop 커버곡의 저작권법적 양성화 방안 연구

It has been observed that K-pop cover works are actively produced and shared on platforms like YouTube and social media.
 However, the act of uploading cover songs to platforms without the permission of the original copyright holders constitutes a violation of copyright law. In light of the objective of copyright law, it is considered more effective to resolve cover culture not simply through sanctions but by obtaining consent from the original copyright holders, thus enabling a win-win s ituation for both the original copyright holders and cover song users.
 The paper identifies limitations in the current copyright enforcement system, including the omission of certain rights from protection. It highlights the need for new policy agreements to address these limitations and suggests that, considering copyright’s exclusive r ig hts, t he u ltimate solution to c opyrig ht i ssues l ies in resolving permission issues between rights holders and content users.
 To address the identified legal gray areas, the paper proposes the “Cover License” system as a model for improvement. The Cover License system leverages the existing general user permission framework to enable original creators and neighboring rights holders to declare their desired profit-sharing ratios in advance and to manage the registration and profit distribution through a trust organization transparently in the public domain. It is expected that this proposal, by preserving the original intent of copyright law, will serve as a w in-win s trateg y, a llowing orig inal r ig hts holders and cover song creators to share profits through a small transaction cost and extend the lifespan of original works.

Cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination

Following the Covid-19 pandemic, the rapid spread of online education and tests demanded the implementation of cheating detection tools to ensure academic integrity. While advances in technology such as face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and IP spoofing detection have shown promising results in detecting fraudulent behavior, their integration raises ethical concerns that must be carefully considered. This work presents a cognitive computing strategy for investigating the ethical implications of using cheating detection systems in online tests. This study attempts to examine the potential impact on students’ privacy, fairness, and trust in the examination process by employing cognitive computing, which models human cognitive capacities. A thorough literature review is used in the process to uncover existing ethical norms and regulatory frameworks linked to online assessments and cheating detection. Soft computing approaches are also used to evaluate the effectiveness and dependability of the aforementioned cheating detection strategies. The study looks into how far facial recognition and expression analysis can go in terms of privacy, as well as the possibility of bias in head posture analysis and eye gaze tracking algorithms. Furthermore, it investigates the ethical implications of monitoring network data traffic and detecting IP spoofing, with a focus on data security and user permission. The cognitive computing model, based on the analysis, presents a comprehensive framework for ethical decision-making when installing cheating detection technologies. The findings of this study contribute to the continuing discussion about the ethical concerns of using modern technologies to identify cheating in online exams. It provides educational institutions and policymakers with practical ideas for striking a balance between academic integrity and protecting students’ rights and dignity. By emphasizing ethical issues, this study aims to ensure that the implementation of cheating detection systems adheres to values of fairness, transparency, and privacy protection, promoting a trusting and supportive online learning environment for all parties involved.

Toward Sustainable Model Services for Deep Learning: A Sub-Network-Based Solution Integrating Blockchain with IPFS and a Use Case in Intelligent Transportation

In the era of deep learning as a service, ensuring that model services are sustainable is a key challenge. To achieve sustainability, the model services, including but not limited to storage and inference, must maintain model security while preserving system efficiency, and be applicable to all deep models. To address these issues, we propose a sub-network-based model storage and inference solution that integrates blockchain and IPFS, which includes a highly distributed storage method, a tamper-proof checking method, a double-attribute-based permission management method, and an automatic inference method. We also design a smart contract to deploy these methods in the blockchain. The storage method divides a deep model into intra-sub-network and inter-sub-network information. Sub-network files are stored in the IPFS, while their records in the blockchain are designed as a chained structure based on their encrypted address. Connections between sub-networks are represented as attributes of their records. This method enhances model security and improves storage and computational efficiency of the blockchain. The tamper-proof checking method is designed based on the chained structure of sub-network records and includes on-chain checking and IPFS-based checking stages. It efficiently and dynamically monitors model correctness. The permission management method restricts user permission based on the user role and the expiration time, further reducing the risk of model attacks and controlling system efficiency. The automatic inference method is designed based on the idea of preceding sub-network encrypted address lookup. It can distribute trusted off-chain computing resources to perform sub-network inference and use the IPFS to store model inputs and sub-network outputs, further alleviating the on-chain storage burden and computational load. This solution is not restricted to model architectures and division methods, or sub-network recording orders, making it highly applicable. In experiments and analyses, we present a use case in intelligent transportation and analyze the security, applicability, and system efficiency of the proposed solution, particularly focusing on the on-chain efficiency. The experimental results indicate that the proposed solution can balance security and system efficiency by controlling the number of sub-networks, thus it is a step towards sustainable model services for deep learning.

N-Accesses: A Blockchain-Based Access Control Framework for Secure IoT Data Management.

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution.

The legal frameworks for protecting the rights to image publication in Saudi and Sudanese journalism; expounding the views of stakeholders

The protection of people' rights in the use of their photos in journalism publishing has been thoroughly examined in this research, with press freedom to access public data repository resources taken into account. Participants in the research were chosen from a cross-section of sectors in Sudan and Saudi Arabia. A total of 832 participants from a seven stakeholder groups in the two nations made up the sample. In Saudi Arabia, 56.85% (473) of the sample came from the six designated stakeholder groups (photojournalists, media editors, staff of governmental agencies, legal experts), whereas in Sudan, 43.15% (359) participated. Using relevant statistical tools, the study made key findings. According to the findings, approximately 50% of Saudi stakeholders and 86% Sudanese participants don't think that consent is required from people before their pictures are utilised in journalistic works. Over 81% of respondents from both Sudan and Saudi Arabia agree that protecting citizens' rights should be done in accordance with the legal framework of the country, but that journalists should be free to use digital resources in their publications. More than 80% of respondents agreed that photojournalists should put the public interest first while taking and publishing photos. It is thus concluded that state authorities must establish legal and ethical norms outlining the duties and obligations of photojournalists. Important considerations like privacy invasion, user permission, and public safety should all be baked into these rules.

Endpoint security reinforcement via integrated zero-trust systems: A collaborative approach

In the rapidly evolving field of information technology, network security, especially enterprise endpoint security, has emerged as a major challenge. This paper presents a comprehensive and interactive method for network access and user authentication utilizing a zero-trust framework. This method integrates key elements, including the Network Access (NA) Agent, Identity and Access Management (IAM) Agent, Policy Enforcement Point (PEP) Agent, and Situational Awareness (SA) Agent, to mitigate security risks associated with critical business information exposure and unauthorized network access. Leveraging a zero-trust approach, the method dynamically controls user permissions, thereby enhancing endpoint security. It also introduces an efficient solution that coexists with legacy infrastructures, balancing security necessities with user accessibility, and offering a unified solution for both internal corporate networks and the Internet. We present a thorough analysis of potential risks associated with this method and propose preventative measures to minimize these threats. We conclude that our method provides a more secure and efficient approach to enterprise network security compared to traditional static rule-based systems, offering a promising direction for future research and implementation.

Establishing a two-way transaction pricing model of “platform-individual” co-creation data property rights

In recent years, the data trading industry has thrived on data assetization. However, given the increasing privacy concerns of the main data providers—individual users—their willingness to disclose data is in decline. Meanwhile, enterprises may violate privacy laws by profiting from co-creation data without user permission, blocking a significant amount of co-creation data from full circulation. This study establishes the utility function of users under three choices—purchase, sale, and maintaining the status quo—that quantify the privacy calculus mechanism and introduce privacy-related variables, employing references and interviews with Tencent employees and users. Accordingly, examining the decision-making behavior of users helped establish a data property rights two-way transaction model comprising six scenarios and, hence, six corresponding platform decisions. Indeed, the model can solve the noted dilemma and realize a Pareto improvement of the utilities of the platform and users. This study contributes a new perspective, constructs a data property rights two-way transaction model, expands the pricing theory boundary, and provides a decision reference for data trading practice, allowing enterprises to establish new business models.

PENGONTROLAN KEAMANAN SISTEM KOMPUTER CLIENT DARI SERANGAN HACKER DAN VIRUS KOMPUTER SECARA JARAK JAUH (REMOTE SERVER) DENGAN MENGGUNAKAN SSH

Computer system security is very important to be considered by computer users to protect their computers from attacks such as hackers and computer viruses that can take data and damage the user's computer system. Hacker is a person or party who has the skill in breaking through and sneaking to access a computer without the user's permission and can take data and even damage the system on the user's computer. And a Computer Virus is a computer program that copies and inserts copies into the program and can damage the computer system. So that by using SSH (Secure Shell) can control and check computer security from hacker attacks and computer viruses without having to come to the location where there is a client computer or done remotely (Remote Server).

Design and Implementation of Intelligent Medical Mobile Collaborative Office Approval System

Medical affairs approval system is a hot issue in the field of smart medicine. The existing medical affairs approval process is unscientific and opaque, and the approval process is slow, resulting in untimely supply of medical resources. To address this issue, a design and implementation method of a smart medical mobile collaborative office approval system is proposed. Based on the Internet big data information collaboration method, it provides heterogeneous fusion and integration of smart medical mobile collaborative office approval data, strengthens the global efficiency of information data analysis and decision-making, and considers improving data security and privacy protection. The system provides multi-level and multi-type user permissions. The test results show that the proposed method improves the efficiency of medical emergency approval, reduces the approval process, and increases the transparency of the approval process.

Blockchain inspired secure and reliable data exchange architecture for cyber-physical healthcare system 4.0

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based Electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner's hand which is backed by blockchain for security and privacy. Our experimental results reveal that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can't access data without user permission.

A study of risk-based authentication system in cyber security using machine learning

The optimum authentication method is determined by the user's risk profile, which is created using context- and behavior-based data from the user's device, finger print, one-time password, and other characteristics. Hacking and security breaches of online accounts, including social networking and web ac- counts, are very common in today's society. We suggest a Risk Based Authentication System utilizing Machine Learning to stop this. For the protection of data and money in this internet environment, security is a worry. Numerous parameters are researched and taken into consideration in the paper in order to solve the issue. These variables determine whether to grant the user permission or not. The gradients descent method is used to verify the user. Previous literature is re- viewed with technical details of the system before conclusion.

A container-based workflow for distributed training of deep learning algorithms in HPC clusters

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters.

Optimization of Access Control Policies

Organizations undertake complex and costly projects to model high-quality Access Control Policies (ACPs). Once built, these policies must be maintained and managed in an ongoing process to keep their quality high. Insufficient maintenance leads to inaccurate authorization decisions and increases the policies’ administrative effort and susceptibility to errors. While the initial modeling of ACPs has received significant research interest, their optimization is not yet covered as broadly. This work provides a theoretical foundation for ACP quality and its optimization. Furthermore, it analyzes how existing research addresses optimization of ACPs with regard to six crucial optimization dimensions. It presents a structured literature survey tracing these optimization dimensions, the contributed research artifact and data requirements. Building on this literature catalogue, this work elaborates on inaccuracies for user permission assignments, data availability, minimal perturbation and recommendation-based optimization.